Tag Archives: incremental lift

Digital Transformation in the Enterprise – Creating Continuous Improvement

I’m writing this post as I fly to London for the Digital Analytics Hub. The Hub is in its fourth year now (two in Berlin and two in London) and I’ve managed to make it every time. Of course, doing these Conference/Vacations is a bit of a mixed blessing. I really enjoyed my time in Italy but that was more vacation than Conference. The Hub is more Conference than vacation – it’s filled with Europe’s top analytics practitioners in deep conversation on analytics. In fact, it’s my favorite analytics conference going right now. And here’s the good news, it’s coming to the States in September! So I have one more of these analytics vacations on my calendar and that should be the best one of all. If you’re looking for the ultimate analytics experience – an immersion in deep conversation with the some of the best analytics practitioners around – you should check it out.

I’ve got three topics I’m bringing to the Hub. Machine Learning for digital analytics, digital analytics forecasting and, of course, the topic at hand today, enterprise digital transformation.

In my last post, I described five initiatives that lay the foundation for analytics driven digital transformation. Those projects focus on data collection, journey mapping, behavioral segmentation, enterprise Voice of Customer (VoC) and unified marketing measurement. Together, these five initiatives provide a way to think about digital from a customer perspective. The data piece is focused on making sure that data collection to support personalization and segmentation is in place. The Journey mapping and the behavioral segmentation provide the customer context for every digital touchpoint – why it exists and what it’s supposed to do. The VoC system provides a window into who customers want and need and how they make decisions at every touchpoint. Finally, the marketing framework ensures that digital spend is optimized on an apples-to-apples basis and is focused on the right customers and actions to drive the business.

In a way, these projects are all designed to help the enterprise think and talk intelligently about the digital business. The data collection piece is designed to get organizations thinking about personalization cues in the digital experience. Journey mapping is designed to expand and frame customer experience and place customer thinking at the center of the digital strategy. Two-tiered segmentation serves to get people talking about digital success in terms of customer’s and their intent. Instead of asking questions like whether a Website is successful, it gets people thinking about whether the Website is successful for a certain type of customer with a specific journey intent. That’s a much better way to think. Similarly, the VoC system is all about getting people to focus on customer and to realize that analytics can serve decision-making on an ongoing basis. The marketing framework is all about making sure that campaigns and creative are measured to real business goals – set within the customer journey and the behavioral segmentation.

The foundational elements are also designed to help integrate analytics into different parts of the digital business. The data collection piece is targeted toward direct response optimization. Journey mapping is designed to help weld strategic decisions to line manager responsibilities. Behavioral segmentation is focused on line and product managers needing tactical experience optimization. VoC is targeted toward strategic thinking and decision-making, and, of course, the marketing framework is designed to support the campaign and creative teams.

If a way to think and talk intelligently about the digital enterprise and its operations is the first step, what comes next?

All five of the initiatives that I’ve slated into the next phase are about one thing – creating a discipline of continuous improvement in the enterprise. That discipline can’t be built on top of thin air – it only works if your foundation (data, metrics, framework) supports optimization. Once it does, however, the focus should be on taking advantage of that to create continuous improvement.

The first step is massive experimentation via an analytics driven testing plan. This is partly about doing lots of experiments, yes. But even more important is that the experimentation be done as part of an overall optimization plan with tests targeted by behavioral and VoC analytics to specific experiences where the opportunity for improvement is highest. If all you’re thinking about is how many experiments you run, you’re not doing it right. Every type of customer and every part of their journey should have tests targeted toward its improvement.

Similarly on the marketing side, phase II is about optimizing against the unified measurement framework with both mix and control group testing. Mix is a top-down approach that works against your overall spending – regardless of channel type or individual measurement. Control group testing is nothing more than experimentation in the marketing world. Control groups have been a key part of marketing since the early direct response days. They’re easier to implement and more accurate in establishing true lift and incrementality than mathematical attribution solutions.

The drive toward continuous improvement doesn’t end there, however. I’m a big fan for tool-based reporting as a key part of the second phase of analytics driven transformation. The idea behind tool-based reporting is simple but profound. Instead of reports as static, historical tools to describe what happened, the idea is that reports contain embedded predictive models that transform them into tools that can be used to understand the levers of the business and test what might happen based on different business strategies. Building tool-based reports for marketing, for product launch, for conversion funnels and for other key digital systems is deeply transformative. I describe this as shift in the organization from democratizing data to democratizing knowledge. Knowledge is better. But the advantages to tool-based reporting run even deeper. The models embedded in these reports are your best analytic thinking about how the business works. And guess what? They’ll be wrong a lot of the time and that’s a good thing. It’s a good thing because by making analytically thinking about how the business works explicit, you’ve created feedback mechanisms in the organization. When things don’t work out the way the model predicts, your analysts will hear about it and have to figure out why and how to do better. That drives continuous improvement in analytics.

A fourth key part of creating the agile enterprise – at least for sites without direct ecommerce – is value-based optimization. One of the great sins in digital measurement is leaving gaps in your ability to measure customers across their journey. I call this “closing measurement loops”. If you’re digital properties are lead generating or brand focused or informational or designed to drive off-channel or off-property (to Amazon or to a Call-Center), it’s much harder to measure whether or not they’re successful. You can measure proxies like content consumption or site satisfaction, but unless these proxies actually track to real outcomes, you’re just fooling yourself. This is important. To be good at digital and to use measurement effectively, every important measurement gap needs to be closed. There’s no one tool or method for closing measurement gaps, instead, a whole lot of different techniques with a bunch of sweat is required. Some of the most common methods for closing measurement gaps include re-survey, panels, device binding and dynamic 800 numbers.

Lastly, a key part of this whole phase is training the organization to think in terms of continuous improvement. That doesn’t happen magically and while all of the initiatives described here support that transformation, they aren’t, by themselves, enough. In my two posts on building analytics culture, I laid out a fairly straightforward vision of culture. The basic idea is that you build analytics culture my using data and analytics. Not by talking about how important data is or how people should behave. In the beginning was the deed.

Creating a constant cadence of analytics-based briefings and discussions forces the organization to think analytically. It forces analysts to understand the questions that are meaningful to the business. It forces decision-makers to reckon with data and lets them experience the power of being able to ask questions and get real answers. Just the imperative of having to say something interesting is good discipline for driving continuous improvement.

foundational transformation Step 2

That’s phase two of enterprise digital transformation. It’s all about baking continuous improvement into the organization and building on top of each element of the foundation the never ending process of getting better.

 

You might think that’s pretty much all there is to the analytics side of the digital transformation equation. Not so. In my next post, I’ll cover the next phase of analytics transformation – driving big analytics wins. So far, most of what I’ve covered is valid for any enterprise in any industry. But in the next phase, initiatives tend to be quite different depending on your industry and business model.

See you after the Hub!