BJREES

B2B marketing and AI

Category: Technology / MarTech

The tools and technology behind modern marketing. Covering CRM, automation, analytics, and the broader MarTech stack — and how it evolves as organisations grow.

  • Building a marketing chatbot pt. 2

    Building a marketing chatbot pt. 2

    I’ve had a lot of fun over the last few weeks building a marketing chatbot. I started here and thought “How difficult can it be?!”

    Two months later I have found out both how difficult some of it is, but also how easy some of it is. The easy bits were putting together sources of information and using ChatGPT to help with the structure of the app. The most difficult bits were anything to do with permissions (even on my own data) and anything where I needed to use Microsoft Graph API. Still a long way to go, but I thought I’d post an update on the tech stack I’ve used so far.

    Screenshot of the app so far! I will publish the real thing when I’ve had a look at the security implications…

    Tech Stack

    The very simple architecture/workflow for the app can be split in half:

    1. Server-side: Run various processes to collect together everything I know about marketing and put it into a database.
    2. Client-side: A way of running queries against that database

    Having these guidelines helped enormously with choosing the technology and also helped to make sure everything worked together.

    I wanted to use the following guidelines:

    • As free as possible. Only pay for something if the cost was minimal and it saved me an enormous amount of time/reduced complexity
    • Linux based. Again for cost purposes, but also because it’s just much easier to use 3rd party libraries

    I ended up with the following, first for server-side then for client-side.

    Server-side

    • Google Cloud. I have used Google Cloud in three places. If you are in the world of Linux then it is so easy to call the Google Cloud API, the online help is superb and it is relatively easy to use. But the biggest winner was the integration between Google Sheets and the API. I thought this would be tricky, but actually you were guided through it very simply. Specifically I used it for:
      • Speech-to-text encoding
      • Cloud Run to run the independent scans of various types of content
      • Google Sheets as document storage, including a sheet which lists all of my blog posts
    • Microsoft Personal OneDrive for documents. This is a significant source of information as I have used Microsoft personal OneDrive for a decade or more to keep notes on various ideas. With hindsight, I would have kept this much cleaner and better governed. There has been a lot of work cleaning up what was in here in all sorts of formats
    • GitHub for storing all Python code. A private repo for now
    • ChatGPT to help me with everything. It would be a lie to say that I wrote everything from scratch. I went to a very impressive lecture recently at the Cambridge Marketing Meetup from Siok Siok Tan who talked about how, going forward, people would work side-by-side with the machines splitting out tasks as appropriate. The way forward for this sort of work is being smart about what you should do and what the machine should do. What I found particularly interesting was the symbiotic relationship needed for writing code. For example, chat gpt wrote quite a lot for me as a starting point but I usually found I had to do a reasonable amount of work on top of that, because the ChatGPT sources were out of date, and APIs seem to be getting updated all the time.
    • Microsoft Graph API. Sigh. I use this extensively to programmatically access my OneNote notes. I had just finished working with the Google technology and thought “Surely the Microsoft APIs are just as easy?”. No they are not. A lot of this is to do with security of course, and that is good as I don’t want everybody reading my personal files. However the structure of OneNote files is nontrivial and getting API access to work correctly there’s a lot of hard work.

    Client-side

    • Pinecone was a big breakthrough for me. I want a chatbot which isn’t just a simple text search. I wanted to try and do something which allowed me to use some sort of fuzzy search mechanism. As an example, my records might say something like “Account based marketing is a great strategy”, But I want that to come up if I searched for something like “What is ABM?”. I needed something more than just simple keyword based retrieval, so moved over to using embedding based retrieval with OpenAI.
    • Docker to containerize everything. Is that a real word!?
    • Google’s speech-to-text API. I’m going to save this for the 3rd blog post, partly because it is just so impressive it deserves a post of its own.

    That will do for now. The next stage is all the testing, particularly to make sure the time using the right security for my documents. One of the key issues with using AI technologies on your own notes (essentially what I’m doing here), is the problem of oversharing information when you shouldn’t. It is something that we help customers with at Syskit, albeit in a different context (Microsoft 365 governance). But the principles are the same – are you sharing things outside your organisation that you shouldn’t be?! Be careful out there… I will eventually put a link in here that links to my app, but not until I have gone through all of the information and documents that I am using to make sure I am not oversharing. I will need to do this by hand so I may be some time…

  • How We Grew Marketing Sourced Pipeline by 20% in One Quarter

    How We Grew Marketing Sourced Pipeline by 20% in One Quarter

    We’re about to go into our quarterly review period at Redgate. We don’t just run QBRs, we also run reviews across all parts of the business. These are a chance to examine the last three months – what worked? What’s going well? What’s not going well and needs fixing? All part of a strong agile process for keeping an eye on what’s really happening, and making adjustments through the year.

    But there’s a flaw in these meetings when it comes to marketing. The implication of this process is that the things we did in Q1 have a direct and measurable impact on the outcomes from Q1. I.e. when I’m talking about our Q1 outcomes, the activities that occurred in-quarter are the most relevant for discussion.

    But that’s a very partial view of the truth. As all marketers know, marketing is a mix of long, medium and short term activities which, if played right, add up to the outcomes we all seek, such as pipeline for the sales teams.

    We’ve just had one of our best quarters for marketing-sourced pipeline in a long time. I can’t say “forever” because we’ve changed how we measure things over the years, but a quarter-to-quarter growth of 20% is something I’m very happy with. I’m not giving absolutes in this chart, for obvious reasons, nor a key to what the colours mean!, but the chart below shows how COVID-19 hit Redgate for marketing sourced pipeline through 2020, then how we’re coming out of the pandemic stronger than ever:

    No alt text provided for this image

    So, great, and I couldn’t be more proud of the incredible marketing team at Redgate for making this happen.

    But we’re trying to build a scalable, predictable revenue engine here. It’s not enough to know “What”, we need to know “Why?” – what did we do to achieve this? What can we re-produce? What activities had no impact, that we can cut? This is a Sisyphean task, full of estimates, best guesses, rules of thumb. And I thought back to a poster that used to be on the wall of our HR department (back when we all worked in offices!) stating “Not everything that can be measured is valuable, and not all valuable things can be measured” – which is particularly appropriate here. But we should at least have a stab at trying to know what influences these numbers.

    Here’s my go, some based on data, some not. And I’ve given these in reverse “timescale” order – from things we did actually do in-quarter, ranging down to activities that have been in-play for years. And of course it’s almost impossible to weight these things – what was most important? What only made a small difference? Very hard to know..

    1. Project to “Leave no lead unturned”. A slightly clunky phrase, but we made an operational change in Feb to hire a couple of great temps who are responsible for passing the right marketing generated leads to the right people in Sales. Like every single B2B company in the world, our internal systems for lead flow are less-than-perfect, so, while we fix those issues over the year, we have two people passing leads over by hand, ensuring leads don’t get lost in various buckets, or in the wrong hands. By doing this we at least know we’re maximising the revenue for each lead we do generate. Timescale: 1-2 months
    2. Fast, agile response to a competitor mistake. Without going into details, one of our competitors made an error, and within days we’d made it clear to our customers that we were there to help them with an alternative if their bosses were asking them to “look around”. We explicitly didn’t “exploit” this error – we never mentioned the competitor’s mistake in any of our comms – but we did make sure that when people were looking for alternatives, that path was easy for them (for example, through comms and campaigns, through sales training – what should people say when these customers make enquiries? – that sort of thing). Timescale: 1-2 months
    3. A launch. We launched a new offering in February. Of course you can’t do a launch every month, or even every quarter (it can call your credibility into question, if you’re constantly doing grand launches for point releases!). We try to do 1-2 “big” launches each year – incredibly well researched, meticulously planned, focused on real customer needs, and when we get this right (like we did this time), these launches resonate really well with customers. Timescale: 2-4 months to plan the launch, but the research goes back much further..
    4. Experiments with digital spend. It’s so easy to just keep spending $$ on the same ads, the same channels (“If it ain’t broke, don’t fix it”). But end of 2020, we decided to do some bold experiments moving budget from A to B, and this has really paid off. What we found was the the efficacy of, say $1,000 is enormously different for different products at different lifecycle stages. Specifically, if a brand is well-established, then digital advertising is much less effective than for a new product or brand. This makes theoretical sense of course, but it’s great for the reality to match the theory. Timescale: 3-6 months
    5. Internal marketing re-org. This is way harder to measure. But, we made some changes in marketing end of 2020, to reduce the friction between different areas and to make the partnership with Sales simpler. Primarily this was about changing the responsibilities for our field marketing teams in different offices, making it easier for them to collaborate with their local sales teams. It’s hard to say “That change made us $500k in pipeline”, but what I can say is that it’s made it much easier to collaborate on various projects (e.g. some of the things above), which I think would have been far less successful without the change. Sometimes your job is to remove barriers and hurdles, rather than add new activities. Timescale: last 6 months
    6. Treating our customers with respect and empathy during COVID-19. Really? That led to marketing ROI!? We made a conscious decision in April 2020 that, during the pandemic, we wouldn’t exploit the situation for our advantage. We could have gone to customers and turned the screw on various deals, trying to take advantage of their difficulties. Instead we decided “How can we help our customers this year? How can we support our customers through this difficult time?”. We started things like the Redgate Community Circle, with a focus on educating customers (so that at least, through the year, our customers could spend time on self-learning). We made sure that, if a customer was struggling to get a deal or an approval renewed, we gave them that time and space, extending trial periods for example. We actively listened to the struggles they were having (e.g. healthcare orgs struggling to keep up with demand) and made sure adjusted our interactions to give them the support they needed – for example, providing additional free support to healthcare companies, no questions asked. Can I directly measure the return on this effort? No. Do I think it was valuable? Definitely. Customers like working with vendors who understand them and their pain. Timescale: 1 year
    7. Moving out of the pandemic. Obviously this isn’t something you can “do” – we as vendors have had zero control over the course of the pandemic. However I think it’s crucial to recognise where your success is a mixture of internal and external forces. In marketing, we have a symbiotic relationship with our customers – we’re always trying to understand their concerns, at the same time as representing what our company has to offer. It’s obviously been beneficial to Redgate that our customers are feeling more confident, perhaps a little more willing to think ahead and getting back to solving some of their long-standing problems. Timescale: 1 year
    8. Long term brand work. We’ve made a number of changes over the years to clearly associate the value customers get from us with the Redgate brand. Specifically, it should be trivially easy to remember “I learnt a lot this year about how to do my job better. It was Redgate that helped me out there”. Or, “I read a great article about what I need to do about problem X at my company – it was Redgate that wrote that”. We’ve done a lot over the last few years to simplify, simplify and simplify again our brands and the associations with that brand, which I think has at least partly led to current success. Timescale – many years!

    Obviously I’ve missed a lot of things here – so many great projects over the last 12 months, small and large. And, as I mention, it’s really hard to to figure out “Okay, but which of these were really important?”. Problem with that of course – successful marketing is a complex combination of lots of activities, hopefully orchestrated together to give the result you need. No single activity is sufficient or even necessary for success, but I think you do need most of these things working together for growth.

    But to go back to the original problem – how do we present a successful quarter as a set of repeatable, scalable activities? Should we just repeat all these things!? The skill is to choose between either stopping, maintaining or increasing these activities. There are a number of areas which are certainly in the maintain mode – the brand work, experiments with digital spend being just two examples. We’ll keep doing these, but not more. And a couple of things you can’t or shouldn’t “continue” – marketing re-orgs are very expensive, and I certainly hope that we need to do less and less work supporting customers during the pandemic – an option I’d be very happy to lose! But the trick is knowing what to double-down on – something I’ll be working on over the coming weeks.

  • Working from Home

    Working from Home

    Lots of people have written about the “New world of remote working” (so much so, that that phrase has become a cliche in the space of six months). But I think it’s still an interesting topic, because I have a hunch the changes we’ve seen in 2020 will become permanent, even as we start to move out of lockdown.

    I agree with the comments many have made, that “The genie won’t go back in the bottle” (another cliche…). I struggle to see a scenario where we’ll all go back to the old way of working – 5 days a week in a single, centralised office. Why? Because it’s working better now from both sides.

    First, compare the old and the new as an employee. Work pre-March was long commutes either pumping CO2 in to the air in your car, or squashing in to a train carriage, losing hours of your day in both cases, to sit in an office and stare at your screen, out for a £6 Pret sandwich at lunchtime, then home, exhausted. If you manage to fit the £80-a-month gym trip in there, great, but you’re home even later.

    Now? Count the ways in which work has improved for the employee:

    • Firstly, the instant time saving on the commute. No commute takes less than 30 minutes, and often much more, if you add in a tube ride, or traffic problems. So many people are immediately getting 1-3 hours of their lives back.
    • Cost of the commute. Either petrol savings, or season ticket. This can easily add up to hundreds of pounds a month.
    • Lunch – eating at home is generally cheaper than buying from sandwich shops. I know that’s tough on the city-based sandwich shops, but more on that later.
    • Health – eating at home can easily be more healthy than eating out-and-about. You can spend 10 mins prepping something in a kitchen, making it much easier to do something good for you.
    • Exercise – going for a run is much easier (with that saved time), or a lunchtime cycle ride if preferred. Pilates classes on Zoom are also easier if you’re not having to find somewhere to change afterwards.
    • Flexibility. A big one I believe – being able to organise your work-life balance is much easier when WFH. A simple example, if your kid needs dropping off at the school gate at 8:40am, you can do that and be back at your desk for 9am. That simply doesn’t work if you have a 1 hour commute to London. So you need to have nannies or lean on friends/family. Additionally, there are benefits like “Being in for the builder/delivery”, getting the washing done, finishing an hour early to go and see family/friends and so on. A lot of this stems back to point 1 – the time saved no longer spent on the long commute.

    Secondly, what about the employer? Unlike the list above, I think there really is only one of note – but it’s a big one, and one that matters to every employer: the cost of running an enormous office, often in an expensive central location. Recently, the city law firm, Slater and Gordon have given up their London office – why keep it? After salaries, office costs are often one of the biggest bills for an org, and it just doesn’t seem necessary any more.

    There is an additional important point here as well – not so much a benefit for employees, but a worry that didn’t turn out to be true. A lot of companies worried about productivity drops in a remote world. But that simply hasn’t been the case – if anything productivity of employees has actually improved, as people adapt to the new ways of working and gain the benefits listed above.

    Seismic shifts like this one tend to only happen when it’s good for “both sides”. Of course there are people who will lose out from this shift – property developers in the cities, sandwich shops in central locations and so on. And of course, I’ve completely ignored some of the problems caused by remote working – isolation for employees, loss of cohesive for company culture, and many more.

    But my point is that I struggle to see a way back. The new world will be one of mixed models – some WFH, perhaps visits to new, smaller offices for 1-2 days a week, perhaps for team events only. Most office work is spent starting at a screen (for better or worse), and that can now be done more cheaply, and with a better use of everyone’s time, with more people doing this from home.

    What impact will this have?

    Firstly, I think the benefits above will become embedded in company working models. Meaning – companies will lower their annual rent costs and related costs (office maintenance etc). And employees will just get used to be able to manage their work around their lives and vice-versa. This will become expected of any role – any job that asks you to be in a central London location for 8:30am will become far less attractive, as employees raise their expectations of working life – suddenly this will become a big negative on a job ad, rather than just an accepted necessity.

    But I think there are two more big additional changes that will happen, one about the towns and cities we all live in, the second about how we look for jobs going forward.

    One of the problems with everyone WFH-ing, is that the whole infrastructure around centralised office work is struggling. If you were running a sandwich shop, book shop, stationary shop, gift shop or similar next to a city law firm in January, it’s hard to see how your business will return. And that really is a shame, particular when your business is failing through no fault of your own.

    But that misses the point that, the need for these services is just the same – it’s just the location has changed. if there’s a movement of, say, 50% of office workers to be at home, then naturally the environment around peoples’ homes will flourish instead. So we’ll start to see new bakeries, new local shops, new cafes sprouting up in heavily residential areas, particularly as lockdown ends and people want to head out for lunch a bit more. This could be a real resurgence for some areas that have struggled to date.

    More than this though, I think we will see different impacts for different areas. I know the South East of England best, and I’d suggest there are two types of town outside London, whose fortunes will change. Firstly, there are cities/towns like Cambridge (where I live now) and Bury St Edmunds (where I was brought up). Both are great places to live, each with their own character. But they’ve always struggled from being just a-little-too-far from London to commute regularly. It’s possible, but a little exhausting. That’s always kept the population down for these towns, because it’s really not commuter-territory if you want to work in London. But that’s not a problem anymore! You can get a job “in London” and now work 3-5 days a week from your flat in Cambridge. This should significantly increase the numbers in these areas, leading to growth of local infrastructure.

    Secondly, there are places which have always been commuter towns – 30 minutes to London by train, but without a whole lot going on there (few local restaurants, cafes etc). These towns could go two ways – either they’ll start to get that infrastructure too, forced by all the local residents (no longer commuting to London), who want nice places to eat and drink. Alternatively (and hopefully not), they’ll struggle as people move away over time – if the only reason you lived there was the commute, and that’s no longer relevant well, why stay?

    The other side to this coin though, and I’d suggest an even bigger change, is how the world of work opens up for you as an employee (and also, as an employer of course). Most job searches start with location – “I’m looking for a Java developer role within 45 minutes of Reading”. But if you remove that constraint, the world is suddenly your oyster! Well, the country anyway. If you only have to visit the office 1 day a week (or even less), you don’t mind a 2 hour commute both ways. Suddenly you can get a role based half-way across the country. As an employee, you can start looking for the things you really care about – the actual role, company culture, the domain and so on, rather than being artificially constrained by geography.

    This democratisation of work, will positively impact the good employers too. Some employers will be worried (“What if all my employees leave, now they can all look further afield?!”). But that misses the point – as an employer you can now look further afield for talent. And this will have a positive effect for great employers who genuinely care about their employees, their culture and the work they do. The knock-on effect here will be that, good employers who don’t allow remote working will find themselves struggling against those that do.

    Again, I want to re-iterate, I’m knowingly ignoring many of the problems that remote-working will bring. I’m not denying those are there. And I think a 100% remote-working world (without any physical interaction with colleagues, either for work, team, or social events) is not something I would find attractive.

    But I feel the benefits of the new hybrid world outweigh the downsides for employers and employees both. And because it’s beneficial for both parties, I think the current changes will stick even once the vaccine is available. Why go back to something less productive, more polluting, more expensive and less flexible, now that we’ve all seen something better!?

  • Building a MarTech Stack at a Small Organisation

    Building a MarTech Stack at a Small Organisation

    I recently spoke at the B2B Ignite conference in London on “Building a MarTech Stack at a Small Organisation: A Real World Example of What’s Worked and What Hasn’t”. Here are my slides from that talk.

    Rules of Thumb

    • Manual first, then automate
    • You’re either growing revenue, or saving costs. Should be able to show this benefit on a piece of A5
    • The business case has to be overwhelming
    • However long/expensive you think it will be to implement – double it
    • Step changes, not incremental improvements

    It’s a lot of pictures, so might be hard to understand without the actual talk! Any questions, feel free to get in touch, always happy to help.

  • Human Beings are Holding Back Machine Learning

    Machine Learning (ML) and AI are big topics right now. Poor Lee Se-dol has just been beaten by AlphaGo – a machine put together by Google/DeepMind and there are numerous other examples in the news.So everyone is interested, and everyone wants to do more of it. Whether you work in marketing or any other discipline, there’s an expectation to be harnessing the power of ML and AI algorithms to provide insight, models and intelligence to applications.

    So, what’s holding us back? Is it the tech – it’s too expensive or not available? I don’t think it’s this at all. It’s been possible to implement ML for decades. You can use R, MATLAB, SPSS, SAS or a ton of other tools  or if not those, Excel or even write your own (I have my own Mickey Mouse clustering app here as I got so frustrated using others’ tools). And people like Microsoft are making the tech more accessible all the time (e.g. Azure Machine Learning). So I don’t think that’s the problem.

    My opinion is that the biggest shortage is in people who really understand ML, and can use it properly. And this is certainly what I’ve seen at customers and companies we’ve spoken to about this problem – they know they want to do it, but they just can’t get the people! Where are these mythical data scientists? Do they even exist? Could we afford them, even if they did? These are the questions we hear.

    The key issue here is that, with ML, a little knowledge can be a dangerous thing. One of the problems with most ML algorithms is that they are complicated. Or at least, you need a significant level of understanding to know what you’re actually doing. If you take a dataset and run a Support Vector Machine over those data points in an attempt at classification, do you really know what the output means? When it gives an unexpected outcome, do you know why? Without jumping to easy (but often wrong) conclusions? Even something easier like k-means clustering – what do those clusters really mean? If there are three clusters, one big and two small, is that really saying something about the fundamental nature of your dataset, or is it just an anomaly because you haven’t transformed your data correctly beforehand?

    These are difficult questions – having the tool available to, say, run a k-means clustering algorithm, is only 10% of the battle. Knowing what to do with that tool is the real issue; and how not to present something to your boss that any smart cookie could undermine in 10 minutes.

    So finding these people is hard. It’s made even harder because I’ve often seen over-inflation with what people mean by “Machine Learning”. In my experience, someone who claims to “Know statistics”, is likely to be comfortable with value/volumes, perhaps a “mean” or a “standard deviation”, but not much more. Those who say they “Understand ML”, often have a good grasp of statistics, but struggle, when things get tough with machine learning. And of course, if you claim to “Know AI” – understanding a Support Vector Machine doesn’t mean you can build the next Skynet!*

    So it’s even tougher finding people who really know their stuff. Maybe it’s just about money – supply and demand. If these people are hard to get, and you’re competing with the City to get them, then maybe you just have to pay the asking price and that’s it.

    Or of course you can pick up a book and start learning! There are lots available – my favourite is Machine Learning and Pattern Recognition by Chris Bishop. A bit older now, there’s a lot in there, and you still need to implement this stuff, but it presents the material in a clear way and, most importantly, it helps you understand what you’re really doing with these algorithms. So at least your conclusions will be based on a deep insight, and not on guesses based on pretty looking graphs..

    * I also have a “Skynet” project in GitHub. Progress is slow.

  • Getting Stuff Done as a Product Marketing Manager

    plates

    I hardly know a Product Marketing Manager who isn’t overwhelmed by his or her workload. As I’ve written previously, this is at least in part because of the vast number of activities that PMMs “should” be doing – how can you not being doing your job properly if you don’t, at least, have a full content marketing strategy, a mobile strategy, a full list of researched and refined personas, a great Twitter presence, a nicely written weekly blog, a monthly webinar, a multi-threaded email nurturing campaign, remarketing, GDN etc etc? All done by Friday if possible?

    So part of the problem is that there is too much we “should” be doing – though as I wrote, I think most of these things should be dropped if only for our own sanity. But that’s only half the problem – the other is the way in which many PMMs work, including often, myself. Even if we’ve restricted our Work In Progress (WIP) down to four items, we still often find ourselves context-switching between those four things through the day – effectively spinning multiple plates trying to keep on top of all of the activities that do genuinely need to be done.

    Context switching is a well-known problem in any field of work. The gist of it is that, by constantly switching between tasks – often including minor admin tasks such as checking and replying to email – you lose a lot more than just the time it takes to switch tasks. You lose the momentum you’ve built up in something, the time it’s taken to get your focus and flow flying so that you can make real progress. And the problem is far worse for difficult or creative jobs – switching between admin tasks (e.g. from email, to filling in an expense form, to sitting in an “update” meeting) is very easy because, quite frankly, you don’t really need your brain for any of these. But trying switching from something like “Think about the core strategy for my marketing plan for next year” to “How are we going to get people to read our articles on X or Y?” – very difficult indeed; and you’ll lose a lot of time trying to figure out “Now, what were we thinking about those articles? Where did I get to last time? Let me re-read my notes…”.

    So it’s a bad thing. But how do you avoid it? I’m trying something at the moment, which is working really well, so thought I’d share it. Completely by luck I read the following book:

    Manage Your Day-to-Day: Build Your Routine, Find Your Focus, and Sharpen Your Creative Mind (The 99U Book Series)

    (It was a random £1.19 offer purchase on Amazon).

    Now half the book – the second half – isn’t great. It’s a little too full of pat phrases about “Creative thinking” and a bit too close to a self-help manual for my liking. But the first half has a handful of really great pieces on productivity and “How to get more creative work done”. My favourite quote, about email:

    RandomReinforcement

    This quote isn’t about context-switching per se, but about the related problem, that’s sucks up as much of our time as context-switching – email interruptions.

    Anyway, the primary bits of advice that I’m taking from a number of chapters, and that I’m currently trialling are:

    1. Figure out what time of day you do your best work. For me this is most definitely the mornings, starting as early as possible.
    2. During this time (in theory, when you’re going to do your best work), undertake a single, important task (say, for 3-4 hour stretch) that requires critical thinking.
    3. No interruptions, period. For me this means email is switched off, office communicator is off, anything on my phone that could interrupt is off, Skype, Yammer etc etc – basically everything.
    4. No meetings. A 30 minute meeting in the middle of a creative period, and you might as well kill two hours.
    5. Push all admin to the afternoon – no email replies (of course you’re not checking it anyway, are you, so you wouldn’t know to reply!?), no updating task lists, no taking 5 minute breaks to check your company Twitter stream, nothing.

    And so far – it’s been great! I’ve managed to write a long blog post, a couple of big plans, think about a couple of quite tough problems all just in the first week. The hardest part is that you have absolutely nothing to do all morning except the single task. And that’s tough if, like most marketing people, you’re used to jumping between tasks endlessly trying to spin the plates. I keep thinking “Surely there should be something else I should be doing now?”. But no – you’ve got the one thing to do, and that’s it.

    But the point is – you’re paid to think, come up with great ideas, solve difficult problems and add some real value. Replying to emails, having catch-up meetings, updating To-Do lists etc are just the necessary part of working in an organisation and, though necessary, aren’t where you’ll ever make a big difference – and if you don’t give yourself time to singularly focus on tasks, how are you ever going to come up with that great, big idea that could turn your marketing strategy around?

  • The Need to Constantly Change in Marketing

    images
    There’s a quote that I really like from one of Christopher Isherwood’s early novels, The Memorial:
    “Men always seem to me so restless and discontented in comparison to women. They’ll do anything to make a change, even when it leaves them worse off. […] Whereas […] we women, we only want peace.”

    Removing the sexism from this quote (it was written over 80 years ago…), gives you something like the following – I’ve removed all of the brackets etc, to make this more readable:

    “Some people always seem to me so restless and discontented in comparison to others. They’ll do anything to make a change, even when it leaves them worse off. Whereas others only want peace.”

    Now I think this quote applies to an awful lot of people and situations, but this is a marketing blog, so why is it relevant here?

    If you step back and look at the ever-changing world of marketing methodology, and look at it over a timescale of years, and really, decades, then the one obvious feature is the constant change in the methods recommended and used over this period. Some examples known to all of us:

    1. The Internet and digital media. I still remember the first time I saw a website address advertised anywhere, on the back of a Björk CD. At the time I had no idea what to do with it (I didn’t have a computer on the Internet) but I know I was impressed. Now of course, over the last 10-20 years, a marketing strategy which doesn’t involve a website and other elements of web presence would be laughed out of the room.
    2. The death of print media. Apart from the very occasional experiment, we haven’t used print media for advertising at Red Gate for at least 10 years. Again, when I was a kid, every video game, bit of software or hardware would buy quarter, half or full page slots in various print magazines and newspapers. This was expensive and, as an advertiser, you had no idea whether it worked or not. In contrast to digital media, a campaign based entirely on print media would struggle to be taken seriously today.
    3. Banner ads. Getting in to something more specific, banner ads are I think the VHS recorders of our generation. It is a “technology” that has both risen and (almost completely) fallen in our lifetimes. Obviously it grew with the growth of the Internet as a medium and was the obvious like-for-like swap for quarter page ads in print media (just scan in your print ad, and send it over to the magazine to put on their website!). But it has the same problems (lack of feedback for the advertiser) and, as we all know, nobody likes or clicks on them. There have been some advances in recent years (using pay-per-click banner ads through Google Display Network), but banner ads are now rarely at the centre of any campaign.
    4.  Adwords. Again, a medium which as grown with the rise of the Internet and Google specifically. Google make an incredible amount of money, almost exclusively from Adwords, and their whole machine is set up to promote Adwords as a necessary and wise choice for the modern marketer (have you ever seen a Google blog post titled “How you could spend a lot less on Google Adwords”?!). Ten or more years ago, the individual who looked after marketing at our company at the time saw how it could be used to massively reduce our marketing spend (compared to print media) and still get the same results (as well as the benefits of knowing what’s actually worked). This was something that was instrumental in the early success of Red Gate, particularly on a limited budget. But could the same be said today? Is Adwords still the most cost-effective way of generating leads, easily outstripping all others? What sort of future does it hold? I’d suggest the jury is out.
    5. Content Marketing. As I’ve written before, hard to find a marketing blog that doesn’t hail content marketing as the new messiah. One group in particular who were very early to recognise its value were the people who run marketing automation companies…

    6. Marketing Automation. The natural progression on from blind content marketing is the use of marketing automation tools to apply that content in the most relevant scenarios, measure the results, then adapt based on feedback. This is an area which is still in its infancy I believe, simply because of the hurdle to getting started (you have to install and setup something like HubSpot, Eloqua or Marketo – no mean feat).

    There are many other methods of course that have had their ups and downs – mobile advertising and social media are also current fashions but the general point is that like everything in the world of marketing these things come and go.

    But, there’s another important thing to note here – there are people who recognise the importance of the new marketing approach before others and are therefore, arguably, more likely to get the full benefit of using that new method first. Björk has always had a great reputation in the world of digital media (her latest idea – Biophilia, a sort of multimedia collection “encompassing music, apps, Internet, installations, and live shows”) is once again at the forefront of what can be done with digital technology (and its great btw!). Bowie is another who was always at the forefront with www.davidbowie.com – its changed many times over the years, but was a pioneering site for fans in the early days.

    Which brings me back to the Christopher Isherwood quote. There are marketing people who, because of their need to always be doing something new are more likely to find the new things that could be valuable for your business. They’ll always be on the lookout for the new trends, what’s coming up in the future and so on. In contrast there are also people who will stick to what they know, and will struggle to try out new things. Each of these approaches has pros and cons – there is a danger, with constantly looking for the Next Big Thing, that we can fritter away our time on endless trends that go nowhere when that time could have been better spent just getting the Adwords campaigns right.

    But the danger with the reverse position – of always sticking to what you know, and ignoring the world around you – is that you stick with something long after its valuable and never fail to capitalise on the new things coming along (in the early days, when you can have most impact). I interviewed someone for a marketing role 2-3 years ago who said “There’s nothing wrong with print media – have you considered going back to that?”. It’s not about whether he was wrong or right, it’s that this exhibited an approach to marketing that I would have really struggled to work with.

    I’ve no idea what the next big trend will be of course. I think there’s another phase in the content marketing/marketing automation marriage where we’ll soon be able to auto-create content for customers based on their very specific needs (imagine a situation where articles could be automatically created from pre-defined blocks of copy, pieced together based on our knowledge of the customer – an article for a large, late adopter pharmaceutical company would be subtly different to that for a small, early majority financial firm), though a lot of these things will require some real, solid output from the Big Data/Hadoop community. But who knows? The point is unless you’re looking for these new trends – or rather, employing people who yearn to find these new things – then you’re almost certain to miss them till its too late.

  • Waiting for Google

    This isn’t going to be a very exciting post unfortunately, though it was supposed to be. I visited the UK Google offices this week to have a chat about the future of Google Analytics, well Google Universal really. I was hoping for some help, some tricks and tips, perhaps a few sneak peeks at a roadmap with dates and maybe some contacts for people who were nearer to the holy grail of marketing attribution than we are, and could help us out.

    First of all, I have to mention the offices (in the newly built Central Saint Giles). Though not a big fan of the buildings themselves, the interiors of the Google offices were fabulous (as you’d expect). The area I liked the most was the library – where the rest of the office is very lively, colourful and conducive to high-octane innovation (or whatever they do there ? ), the library was this wonderfully quiet, brown-carpeted and wood-panelled space, which felt like a great place to really get your head in to a problem without the distractions of email, Twitter, Facebook, Yammer, LinkedIn, Youtube, RSS feeds and everything else that stops us doing real work.

    But, that aside, we were there to talk about the issues we have with Google Analytics (GA), for help with the endless problem of marketing attribution, and also to talk a bit about social media.

    And they do have a lot of great stuff coming up in Google Universal (GU from here on). The vision is obvious in its simplicity – to break down the barriers that exist between silos of information whether online or not. So today, when we look at the attribution models that exist there are a lot of things missing in the model (a lot of these are straight from Avinash’s posts – who works for Google as well, particularly his post here):

    1. Offline activity before online activity that contribute to lead generation, including –
      1. Events and conferences,
      2. Word of Mouth,
      3. Print ads and direct mail,
      4. TV, radio, posters etc,
      5. (Potentially) product trials
    2. Offline activity after online activity, primarily sales that take place on the phone or by email (i.e. not through a browser tracked by Google),
    3. Activity happening on different devices (right now, GA doesn’t pick up that the customer who bought yesterday, actually did lots of research on his/her iPad just before purchase) – phones, tablets, home PCs vs work PCs, other browsers even.

    One of the key things they’re trying to do as well, which was very interesting, was to move away from viewing Visits, Unique Visits or even Unique Visitors and instead to consider the Customer as the central key for the data. “Unique Visitors” is a proxy to this, but suffers from all of the problems mentioned above – if that individual customer browses sites on a mobile device, found out about you at a conference and eventually buys on the phone (with a brief visit to your site, somewhere in the middle), then you still don’t really know what happened, even if using Unique Visitors as your focus.

    So this was all very interesting and all very exciting. But what I realised about half-way through the conversation, was that so much of what they’re offering is still very much in the future – hence the title of this post. There are many things that are theoretically possible, but I’m afraid they were a little short on real and useful implementations that we could take away and use. Two problems for which we didn’t get great answers were:

    1. Attribution of offline activity such as an event. I was really after some great best practice advice and to find out how others had fixed this problem. Instead it was mainly just some quite obvious suggestions (“Have you tried a specific redirect URL for customers at an event, such as www.red-gate.com/NYCEvent2013?”) which we already knew,
    2. Integration with CRM systems so that customer data could be pulled in to GU and used to segment better. E.g. if you could add in your own knowledge of your customers to GU – for example, what sector they were in – then this would be a great way to split out your attribution to suit your own business. You could display things like “Show me what marketing worked for the Insurance sector vs. Healthcare”. But again, this is a way off. In theory it can all be done right now, but there are no pre-existing add-ins for Salesforce, SugarCRM, Dynamics (all the standards) as yet. If you want to try it, you’ll have to have a crack at using the APIs yourself.

    So my last question to them was basically “Can you give us a feed or channel through which we can find out when these things are going to be available?” – and this answer was actually useful. The best place is the training and thought-leadership events and conferences that they run about GU, as things will often get discussed at these events that aren’t firm yet, that aren’t quite ready for public consumption and so it’s a great place to hear the buzz. If I go to one of these sometime soon, I’ll report back if its useful!

    So overall, a little disappointing, as I’m quite a practical person and wanted to come away with something I could implement the next day (as an aside – always a great rule of thumb for training/educational events – if people can leave your conference and implement something the next day, that’s a great success). I’m excited about what they’ve got coming, but I really want to see the worked examples, the pre-written integration packs, the services that seamlessly plug in and “just work” for measuring offline activity. When these come, I think we’ll really start to see the true power of something like Google Universal.

  • Measuring Offline to Online Marketing Attribution

    sisyphus

    This post is about one of the many issues facing anyone trying to do marketing attribution – how do you measure the impact of offline activity on online success? If you’re selling online, but you’re carrying out offline activity (TV ads, magazines, direct mail, events, arguably word-of-mouth) then you don’t get this sort of insight “out-of-the-box” with Google Analytics – well, not yet at least!

    Avinash Kaushik (@avinash) has written a great post here, describing the problem in a simple way. Essentially, how do you figure out where all of that Direct and, in our case at least, most of the Organic traffic is coming from? I’m not showing figures, but the snapshot below shows the five most important channels for part of Red Gate’s business:

    Top 5

    NB: This is based on a channel grouping and attribution model that I described in a previous post (I’ll write another post later about the set up of this model for us). It’s not “Last click” attribution by a long shot (and so doesn’t suffer many of the problems of that model), but does still present an issue of how to get some insight in to where traffic is really coming from.

    For us, the vast majority of “Organic” is people just typing in the names of our products or company (i.e. it’s not SEO as such, it’s just people who can’t be bothered to type in “www.red-gate.com”). And Direct is the black box that it is for everyone else.

    Having lots of this Direct and Organic traffic is great of course! Everyone knows us already – maybe we should pack up, go home and leave the traffic to roll in!?! But, for me this isn’t really enough. Knowing that there are stacks of people out there just typing in our product names, without having any idea what we’ve done to influence that is tough. Particularly as we spend a lot of money on events and other offline activities – we need to know the impact of these.

    Separately, I’m about to start helping with a piece of work for a large direct mail provider to help them prove the importance of direct (postal) mail in an online world – they need to be able to prove this impact (it’s not just enough to say “Of course it works – it always has!”, when everyone else is using Google Analytics to prove the impact of online activities). They need to link direct mail to Google Analytics – Universal Analytics really – so that customers can show the way that direct mail drives online events and eventually revenue.

    Anyway, here are the things we’ve done historically to try and address this (most of which are as Avinash has suggested) and then want we’re going to try in the future.

    What We’ve Already Tried

    1. Doing it by hand. As described in an early post of mine, it’s possible to go through, by hand, and track all of the revenue generated by a specific offline activity. E.g. “We did that event in Colorado, let’s manually track everything that happened with the people we spoke to”. This does have the advantage that, data problems aside, you should be producing a pretty accurate picture of the ROI on a particular activity. The downside is that it’s very manual. And worse – it only tracks activity for point-in-time activities. What about the general background activity going on – word of mouth, ongoing ads/campaigns and so on?
    2. Redirect URLs on offline material. E.g. we go to a certain event, and the URL plastered over everything is something like “www.red-gate.com/agile2013event” or similar. This then redirects to a URL with UTM codes so that we can use GA to see the impact. Again, this works well for specific events, just not great for general background activity.
    3. Ask people on the website! Again, another of Avinash’s suggestions, but we have questions on our download forms, asking people where they heard about the product:FirstHear
      This has the significant advantage that it includes things like “Recommend by colleague” as well as other offline activities such as tradeshows. Also, by mixing online and offline answers in one question, this gives you some measure of relative importance of different activities (even if absolute numbers don’t mean much). NB: A number of people leave this as “Please select..” (we don’t force people to answer), so it’s flawed, but the info is still very useful. NB: Around half of people in total answer either “Recommended by colleague” or “Used in previous role” – get a great product and half of your marketing job is done for you..

    All of the above have provided interesting bits of information, but none have provided anything like the data we really need to accurately compare online with offline marketing and to treat all sources equally. As I’ve said before, I suspect this is a Sisyphean task, but that shouldn’t stop us trying.

    What We’re Going to Try in the Future

    There are two approaches we’re going to try next – one based on Google Universal, the other with HubSpot.

    1. Google Universal. I’m really excited about the possibilities of Google Universal Analytics. It’s still only in public beta, but the most interesting parts are to do with allowing companies to collect data about offline activity then load this up in to GA like any other channel. Previously it has been possible to pull in data about, say, an event and track that manually (my option 1 above), but without it being integrated in to GA, it’s hard to weigh different activities against each other accurately. We’re looking at using this for a number of different types of offline activity in the coming months and will report on progress! It’s also the key method I’ll be suggesting for the direct mail initiative – how great would it be to be able to show the impact of direct mail accurately alongside SEO, PPC, videos, content marketing and so on?
    2. HubSpot – an alternative, but very related approach is marketing automation. I’ve written about this before, in relation to Eloqua’s product, but marketing automation tools allow you to automatically start tying various activities (offline and online) to the good leads that you generate. The dream (which I know will take work to achieve) is that when we’re looking at a great lead that’s come in to the system, we’ll be able to see what that person has seen and done beforehand, for example:
    • Attended event where we were exhibiting,
    • Read some articles on our sister publishing sites,
    • Searched for some products,
    • Found our site, read the product homepages, and downloaded the whitepaper,
    • Came back a month later and downloaded the product,
    • Tried the product and used the key features,
    • Came back to our site and clicked on the pricing page.


      …at which point we’d probably give him or her a call! At first glance it may seem that only one of these is an offline activity (the event), but actually things like “Trying out the product and using key features” are also offline in the sense that they’re not immediately trackable in web activity. Products like HubSpot provide APIs that allow you to feed in other data that isn’t immediately available so that you get a fuller picture of what the lead has been up to.

    We’ll see how both of these methods work out though both do rely on the same thing – some mechanism for indicating offline activity online, either in GA or HubSpot. However one great thing about HubSpot in particular is that we already track a lot of offline activity in our CRM (for example, adding references on tags for people who we meet at conferences), and this could easily be pulled in as part of the history for a lead. I.e. if we’ve recorded in our CRM that Joe Bloggs went to our conference a year ago and now, a year later, is reading our whitepapers, then this will automatically get shown and we can, in theory, start automatically showing the value of this offline activity.

  • Five Tips for Implementing Marketing Analytics

    Competing on Analytics

    The book Competing on Analytics by Thomas Davenport and Jeanne Harris is a short but very interesting read about the need for organisations to significantly improve their analytical capabilities if they want to compete in the modern marketplace. The argument, quoting directly from the author is that:

    In today’s global and highly interconnected business environment, traditional competitive differentiators-like geography, protective regulation, even proprietary technology-are no longer enough. What’s left is the opportunity to execute a business with more efficiency and effectiveness than your competitors, and to make the smartest business decisions possible. Analytics can help do this. 
     

    I.e. unless you are implementing and using advanced analytics, you’re going to be left behind because you can’t use some of the traditional differentiators to keep any sort of advantage. NB: I don’t discuss Big Data in this post at all, as it’s more about the why rather than the how. But probably a post there for another time..

    One of my favourite quotes from the end of the book is:

    Analytical competitors will continue to find ways to outperform their competitors. They’ll get the best customers and charge them exactly the price that the customer is willing to pay for their product and service. They’ll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel, and their customers will be loyal in return. Their supply chains will be ultraefficient, and they’ll have neither excess inventory nor stock-outs. They’ll have the best people or the best players in the industry, and the employees will be evaluated and compensated based on their specific contributions. They’ll understand what nonfinancial processes and factors drive their financial performance, and they’ll be able to predict and diagnose problems before they become too problematic. They will make a lot of money, win a lot of games, or solve the world’s most pressing problems. They will continue to lead us into the future.
     

    What a great place to be! But of course it’s not as simple as that. So below are a number of tips I’ve picked up from trying to implement this sort of thing over the years, or watching clients trying to do this as well.

    1. Fit the implementation to your real needs. One of the first things to note is that a lot of the examples of success that they give in the book – Amazon, Netflix, Google and so on – are big companies. These organisations have millions of customers and, at that scale, the benefits of well-embedded customer analytics are obvious – Tesco Clubcard is another great example of significant profits generated through analytics.

    So firstly, I think it’s a bit of a struggle implementing many of their ideas when you’re running at a much smaller scale – even if you thought it beneficial. One of the companies I worked at a few years ago had a total potential market of precisely 29 companies. If we wanted to know anything about these companies we didn’t look to data analysis to understand patterns of behaviour, we went to see them and asked!

    2. Start Simple. A second more subtle point here though is about making sure you pick off the low hanging fruit before moving on to anything advanced. One of the questions I was asked at a job interview a long time ago now, was to do with detecting fraudulent activity on bank accounts. The question was “If we gave you the data showing the amounts going in and out of a bank account each day for the last 3 months, what’s the first thing you would do to try and spot fraudulent activity?”. I’d just finished a maths course 2 months before, so I launched in to a tirade about algorithms for picking out outliers, spotting complex patterns in the data, weekly seasonality calculations, de-trending the data and so on. After a few minutes of this, the interviewer interrupted and said, “Mm. I’d probably just draw a graph and see what was there”. I did manage to get the job in the end and found out that a reasonable proportion of the “advanced analytics” needed to detect banking fraud was really very simple algorithms to spot outliers, pretty close to what you’d be doing by eye (think “3 standard deviations away…”).

    An example from the world of marketing – you might be trying to figure out “What type of customer in our CRM system is more likely to stay with me long term, continuing to buy products? (i.e. high LTV)”. There might be a lot of subtle factors here, but there could be some real no-brainers. For example, I’d suggest that customers who have spent a lot with you in the first 3 months are more likely to spend a lot in the future (because the initial spend is indicative of certain levels of budget and/or appetite for your products) compared to someone who spent very little. Of course you have to test this in the data (because it could be completely wrong – see the quote at the end about the blocks to implementing analytical thinking), but if you wanted a simple model for “Which accounts should we spend more time with?”, a simple binary model of “Spent more than $100k with us” vs. “Spent less than $100k” might be a good first start!

    3. Get some early wins. There is always a (not completely unreasonable) objection to analytical marketing along the lines of “This stuff is all well and good, but if we spent more time just putting together some great ads, some great messages, reaching out to the community, running some great promos etc, then the money will flow. We’ll worry about analysing the detail later on”. Sometimes this comes from a certain mindset (again, mentioned in the quote below), about “the power of ideas over data”. Sometimes  it comes from seeing failed implementations. I have a lot of sympathy with the latter – it is very easy to spend an enormous amount of time and money on this sort of project – time that could be spent elsewhere in the business – and see precisely no advantage at the end.

    One way of combatting this problem is to make sure you get some early wins, even if these aren’t the primary purpose of your project. For example, you may have a vision of an all-encompassing CRM system that knows exactly the right email to send at the right time to the right customer (“Dave, we know you’ve been enjoying our product for 19 days now and that your boss, Helen, is interested in how the product could help her with regulatory problems in the textiles industry – here’s a whitepaper answering her questions and a quote for a price that I know exactly fits her budget for this quarter.”). But if you wait for utopia, without showing some earlier, simpler wins, you’ll be working for years battling off disgruntled executives with other priorities.. So better to pick an early problem where you know you can win. If you’ve never sent any segmented emails at all, then start with a simple opportunity (like sending different emails to new vs. existing customers based on some analysis) then prove that this has had a positive impact on either outputs (such as click rate) or (much) better still, revenue. Once you’ve proven positive return, then move on to the next stage with something a bit more clever.  I’d use the phrase “Build success on success”, but it sounds far too cheesy.

    4. Analyse off-line first, on-line later. There’s a great model for step-wise implementation of CRM analytics, along the lines of:

    a. No analytics – purely transactional CRM system.

    b. Offline analytics (e.g. showing that you need to treat new and existing customers differently), implemented manually (sending emails by hand once a week).

    c. Offline analytics implemented automatically (e.g. above analysis embedded in to CRM/email system such that different customers automatically get the right emails. Or offline analysis shows that certain types of customers prefer particular products/prices, so these are hard-coded in the system),

    d. On-line analytics, automatically adapting based on data coming in. For example, your initial model might say that “Customers who come in from Northern Europe are more likely to purchase product X” but, as data comes in, you find this changing over time. The models in the CRM automatically pick up that Southern Europe is now more likely to purchase product X, so automatically adapts to offer this product to these customers instead.

    The last options is, for most, a complete pipe-dream, and can actually be quite dangerous. Therefore I’d strongly recommend a model where analysis is carried out off-line, on sets of data, then the conclusions from that analysis are proven online first. E.g. your offline work shows that customers in China are more price-sensitive than those in India? Implement this by just hard-coding different prices for the two countries and seeing how you get on..

    5. Ignore all of the above if you’re working in a SaaS environment! A lot of the caution above stems from trying to run before you can walk. As I say, if you only need to sell 2 widgets this month to break a profit, your time might be better spent phoning up all of your potential clients, rather than analysing their behaviour to the nth degree. But – the SaaS world disrupts this approach. If you’re not analysing behaviour, interactions, price sensitivity, churn rates, click-through rates, conversion rates etc etc etc from the start, then you’re putting yourself in a very precarious position. Customer service and sensitivity to customer needs is everything in the SaaS world and if customers aren’t getting what they want, they’ll leave you and move elsewhere (the downside of easy adoption is easy rejection).

    Hopefully some of these tips are vaguely useful. With regard to the book – it describes some interesting principles, and is good at a certain level (quite high level), though is a little short on detail. Nevertheless, I thought I’d end with my favourite quote, about the factors that hinder adoption of advanced analytics in organisations:

    Gary cites four common factors that hinder analytical competition: deeply embedded conventional wisdom that has been around for so long, it’s hard to reverse; decision making-especially at high levels-that fails to demand rigor and analysis; employees themselves who are not willing or equipped to do analytic work; and the power of ideas over data.