Let's update an old data analytics maturity model

OCX Cognition CEO Richard Owen used an analytics maturity model I had not seen before in his deck for a webinar this week. As far as I can tell, it originally comes from SAP, way back in 2012, and quite a number of people have adapted it for their own purposes. I think it works really well for customer analytics and machine learning, and suggest considering the model as having three relevant levels.

First level: the current state of CX analytics in most companies

Most companies mainly depend on surveys for customer analytics. You get the data, clean it up if necessary, then produce a series of standard reports, the same ones you prepared last quarter or last year. When presenting to specific audiences, you customize the output. The only substantial question you ask and answer is “How is our performance at the moment?" I suppose it would be a bit more accurate to say that the reports provide information on recent performance, rather than what is happening right now. If you are producing overall brand-level reports, the information you provide does not correspond to the way any individual team measures itself; you are just hoping the CEO will see things the same way you are presenting them.

Second level: basic root cause analysis

More advanced companies and CX leaders add basic root cause analysis to their studies and reports. If the overall brand-level results have improved or deteriorated, you provide the basic information about why. This tends to be quite high level, mainly because the information received from customers is not very detailed. For example, you may have determined that the customers who took your survey were less happy than at the same time last year, and that there was an increase in negative comments about product delivery performance. However, you don't have the data needed to determine whether this was because products were not in stock, were delivered late, delivered damaged, whether a delivery was simply incorrect, or whether the paperwork did not match the delivery. The analysis is basic and a lot better than nothing.

Third level: Customer AI

The third level uses the best that modern technology can provide: Customer AI. It overlaps previous phases somewhat, so let's cover it from the start:

• The raw data it uses goes well beyond surveys, adding the customer-touching operational data that is already in your IT systems, and is of course constantly updated. Just like survey data, it does need to be reasonably clean and new implementers sometimes discover that operational data points they have been using for years are simply incorrect.
• The standard and ad-hoc reports change, as they are based mainly on the operational data. NPS and things like Net Recurring Revenue are predicted at the top level from the operational data points and trends. Those changes are then attributed back to the most significant trends in the operational data. All of the reports therefore express results in terms of the KPIs and other data points that each relevant team already uses. The root causes are no longer vague and mysterious.
• In addition to generating insights about the current state of play for each and every customer, Customer AI adds predictive analytics, meaning insights about what will probably happen with each customer in the future, for example, how likely each one is to renew their current contract and to buy more from you.
• Finally, the insights extend to optimization, from a number of perspectives. At an obvious level, it allows you to prioritize investments in operational improvements since you can see their overall impact and ROI for your entire company clearly, rather than just the subset that an individual team may care about. Perhaps less obviously, you will now find it easy to identify Ideal Customer Profiles, meaning the attributes of customers that cost the least to sell to, and have the highest probability of renewing their contracts and buying more. You can use this data to greatly improve the effectiveness of both your sales and your marketing teams, at the very least.

Conclusion

Perhaps surprisingly, a relatively old data analytics model is still relevant in our new and expanding world of machine learning. When the model was developed, predictive modeling and optimization were at least theoretically possible, and could be done semi-manually. Customer AI has made them both fast and effective, and implementation is usually possible without increasing costs compared to now-obsolete survey-based analytics. So why not get in motion now, delivering your first results in Q1 2024?

Notes

OCX Cognition predicts customer futures. Our breakthrough Customer AI solution lets enterprises transform what’s possible in customer experience. Reduce your customer risk, break down silos, and drive speedy action – when you can see what’s coming, you can change the outcome. Building on more than 100 years of CX-focused expertise in our small team and thousands in teams we have led, we’ve harnessed today’s advances in AI, elastic computing, and data science to deliver on the promise of customer-driven financial results. Learn more at www.ocxcognition.com.

Maurice FitzGerald is Editor-in-Chief, Content at OCX Cognition. He retired from HP where he was VP of Customer Experience for their $4 billion software business and was previously VP of Strategy and Customer Experience as well as Chief of Staff for HP in EMEA. He and his brother Peter, an Oxford D.Phil in Cognitive Psychology, have written three books on customer experience strategy and NPS, all available from Amazon.