The data science, customer and marketing analytics and general all-things-understanding-data hype shows no sign of fading away. Yet lots of articles and posts still point to organisations being frustrated that they’ve invested a small fortune in technology but aren’t seeing the returns on investment the glossy sales brochures promised.
So, what’s getting in the way?
I think the key is often that data science is seen as a technology project: build the right database and buy the right software and everything will just fall into place. It doesn’t take long to learn that it’s all a little more complicated than that – it’s really all about treating the investment in data science and advanced analytics as a larger change programme.
I believe customer and marketing analytics is vital for successful organisations. In fact, getting this stuff right is no longer a competitive advantage in many sectors – having a core competence in this area is now almost table stakes.
So, what should you be looking at to make sure you get it right? And how do you move from the technology project to a wider change programme that will drive real benefits?
I believe best practice organisations have 3 key things in common.
- They have access to the best data
- They have the best ability to generate insight from that data
- They use those insights to execute best at moment of truth
Access to The Best Data:
This is about getting your data strategy right. Success will be driven by having the right data to enable delivery of your customer and marketing strategy. Never start with “we need a data strategy” as if this will be the Holy Grail. The starting point should always be “what are our strategic goals and how can data and analytics help us achieve them”? And the focus should always be on the BEST data not the MOST data. Lots of great articles have been written about big data but real value won’t be created by just having lots of data, it will be about having data that is accessible, well curated, timely, accurate (master data management matters, a lot) – and it’s this that generates insights.
Best Ability to Generate Insight:
Whether insight provides the eureka moments that support senior decision making, whether it’s BI to support operational decisions or whether it’s machine learning and model outputs that drive automated customer communications in a CRM system, the data itself is vital BUT it’s a raw material. That material needs to be crafted into an end product. This needs talented analysts with the right tools. Spending small fortunes on a new data lake but not putting investment into developing your people is a mistake that has sunk many analytics projects. Investing in things like competency frameworks, training and development programmes and in using tools such as “Great Place to Work” colleague surveys* to understand the needs and desires of analysts are key to success. It’s no use investing a fortune in a Formula One car with a motley pit crew and a learner driver…
Execute Best at Moment of Truth:
How do you make the rubber hit the road? However great your insights, they need to drive action. Do you have the right operating model and processes? Does your C-Suite consider customer and marketing analytics on a regular basis and does it have a place high on their agenda? Does marketing really make every key decision based on analysis and insight, rather than just “intuition”? Does the analysis team provide input to key forums or does the head of analytics actually have a seat on those forums? As much emphasis should be placed on the application and translation of ideas as on their research and development if you are to avoid ending up frustrated that the outputs remain locked away in the back office and don’t seem to drive actions.
Driving action requires building an effective operating model: in isolation, data science becomes nothing more than an interesting intellectual cul-de-sac. Get your target operating model right and you can get your company in the fast lane.