Moving beyond CRM: What telcos need to know about advanced customer analytics strategies
Customer analytics is a vital part of setting the direction of any telco’s marketing activities. Without a clear picture of who your customers are, any campaign you run won’t be effectively targeted or personalized to the degree that today’s consumers now expect.
This means both less return on investment for individual campaigns and less satisfied customers overall, who may be more likely to churn in favor of a more engaged competitor.
But just because you have an analytics program, does this mean you’re getting the right results from it? In many cases, continued use of legacy tools could be holding back telcos, as they do not offer results fast enough or at the scale firms need. What’s more, traditional forms of analytics may not fully utilize the valuable first-party data they have available.
The limitations of CRM analytics
Often, telecoms customer analytics programs remain reactive – what’s usually referred to as ‘descriptive analytics’. This tells you about your customers’ past behavior and may allow you to infer future trends, but it won’t let you get ahead of the curve or make accurate predictions about what customers might want in future.
Another issue is that a statistical approach only works with small datasets where the relationship between variables is clearly understood. However, as the size of datasets grow and the number of variables runs into the hundreds, the impacts they have on each other becomes far more complex.
Such an approach does have its uses. For example, it can help you segment customers into categories, show you who your most and least profitable customers are, and see how quickly any issues were resolved.
However, it can often be difficult to integrate this with other sources of data. For telcos in particular, this means valuable weblog and behavioral information. If you are limited in the type of data you can include in your activities, this makes it less accurate and leaves you lacking visibility into key metrics.
For instance, it means that if you are using CRM data to forecast future trends and determine which customers will be most receptive to a particular offer, you are doing so without valuable insight into what interests someone has, or the brand affinities that mean the most to them, that may affect how they respond to specific types of marketing campaign.
In other words, while CRM analytics alone can often tell you the ‘what’ about your customer – what are their spending habits, what devices do they favor etc – they don’t tell you about the ‘why’. And knowing this is essential if you’re to deliver relevant messages that they are more likely to respond to.
The other customer analytics options available
One solution to this is to invest in a Customer Data Platform, or CDP. This promises to offer telcos a much more in-depth view of their customers, combining CRM data and information from other first-party sources, including critical behavioral data.
A good CDP offers a few key benefits. It unifies all your first-party data into a single platform; it allows you to take better control and management of this data; and it helps you turn the insight this provides into action.
However, even a CDP solution won’t give you everything you need to fully understand your customers and run truly personalized marketing campaigns that provide relevant offers to the right people, at the right time and in the right way.
For instance, one problem with these tools is that they typically require a large amount of technical knowledge. This makes them the preserve of specialist business intelligence or data science teams, adding more distance between the data and the customer-focused teams who will actually use it.
The benefits of a full Customer Intelligence Platform
Therefore, to allow any users to dig deeper into your data and find actionable insights, you need to embrace advanced options such as a Customer Intelligence Platform (CIP). This can be considered as the next step in the evolution of CDPs, taking what they do well and enhancing it with more advanced technologies.
A CIP system enables you to take advantage of sophisticated analytics techniques such as machine learning (ML) and customer artificial intelligence (AI). This automates many of the most time-consuming and complex processes and helps you work much better with customer data quickly and at scale. ML also makes it possible to better understand how hundreds of variables affect each other, giving you a fuller picture of what’s going on within your business.
Crucially, however, this is a solution that is able to be used by everyone. With the right intuitive, easy to use platform, marketing and customer experience professionals are able to explore and ask questions of this data without the need to rely on experienced IT and data science professionals.
This matters because it ensures you can take advantage of both the data insights provided by the AI and ML-driven platform, and the experience and understanding of your marketing and customer experience teams. With these platforms, they can test their own ideas and theories against the data, run experiments and most importantly, put these insights into action quickly, scaling up successful campaigns, without having to wait for data teams to conduct analysis or review results.
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