I wanted to talk about a subject i really do enjoy and believe will become predominant in 2016 : the rise of predictive analytics in the CRM world. But before a little reminder of where we are at with CRM today.
For the past decade, companies were looking at ways to better serve their customers. So naturally, they looked at ways to centralize their information, gather the knowledge to better know their clients, and CRM was the solution they chose. After the decision came the integration part, the time and effort required to put the technology in place, which actually we learned wasn’t as bad as making sure the product was fully adopted by the organisation…User adoption is a key factor in the implementation of such tool, and many integrators underestimated this point.
As the industry started to realize the potential of CRM, a natural shift occurred, and from a repository of information, the CRM became a tool to sell and manage. Marketing would generate leads through different mechanisms; these would flow to sales which would qualify, generate and manage opportunities building a sales pipeline, and informing the business as to what to expect in the future. Of course, we would now see our clients interacting with us, involving the support department which would also leverage the CRM to better serve their customers…as they do so, we now know even more about our clients, so much that we now start talking about having a true 360 degree profile ! CRM then started to integrate with other critical tools in the organization, such as ERP’s to bring the financial situation in the picture, so we can now compare the forecasted with the invoiced, because as we now have a CRM, we are now able to look a little further into the future.You see where i am going with this : the CRM today becomes a natural placeholder with everything that is client related because it’s easy to see, easy to report on, it’s a tool to manage and it’s easy to integrate.
Now that I have everything related to my client in my CRM, I also have the ability to analyze the business in many different ways, easily. Complex IT eco-systems meant large scattered pieces of software spread across the globe, expensive and complicated to implement, maintain, and integrate. Not to mention that getting reporting from different sources to try and get an accurate picture of the business and the clients would be a long and complex process. But since companies have now all that data integrated in CRM, crunching numbers become much easier given the simplicity of the reporting tool nowadays and anyone today can easily create a report from CRM, embed it on a dashboard, and share it with the team.
Integration is a major factor of why companies now do everything in CRM : today, there is a connector for everything, and if there isn’t any, web applications are able to push / receive messages talking the same language. So really, the simplicity of integration is also a contributing factor to the fact that CRM has a great success in the industry today. And since my CRM should tell me everything I know about my client, I want it all in my CRM ! It all comes back to the 360 degree profile : yesterday, I wanted to know my client’s basic information, what he bought from me, the nature of our conversations, who he is… One example i often laugh thinking about is a salesman one day asking me to capture his client’s favorite drink so he can impress during meetings. Well nowadays, not only do we want to know who you are, but we also have potential access to tons of additional information about you that we also want to capture : your tweets, your interests, your pictures, your friends, your hobbies, your website visits, your keyword search..all that information is online and accessible, and can be stored in CRM when appropriate. We are data, and the Internet of things only concurs to expand your footprint through any device you use. And as the world progresses, we will only leave more behind us. One source of data that will become much more significant i believe in the next year or so will be bio-data. Most devices have a GPS now, pedometer, some smart-watches have a heart rate monitor..That’s also data that will one day feed a CRM and complete your profile.
So that’s the interesting part. Now we know your buying patterns, your browsing habits, your frequency of reply to emails, etc..we can start building a mathematical model that combines actual data with expected output, and try to “guess” what will be the result if you influence input parameters. For instance, you know you sold X amount of computers to X amount of customers. these customers each have tons of data (attributes) that are static at the time they buy, but that you can monitor to start drawing a pattern. Also, you know that marketing these customers by phone ended up increasing your sales by X. So by building a well defined model with assumptions, you could predict based on the data how much more computers you would sell next month if you increase your phone marketing by X.
Another example : you know clients came to visit your website, spent X time on this or this page, were prompted with a rotating popup (different each time), and depending on their gender, age and location, some were more inclined to sign for a newsletter on your page than others. You could then run a predictive model to guess based on the attributes and numbers of your lead what banner is most likely to make thm sign to the newsletter.
So you get the idea of predictive analytics applied to CRM. I could be a salesperson looking at a profile of a lead i’ve never met before, never talked to before, yet the CRM based on data, patterns and assumptions could suggest me the best time to call to close a deal, the best sales pitch to use to fit my client needs, or list the client’s browsing pattern / buying pattern even before i have done my initial contact. It could help marketers increase the efficiency of their message, with contextual messages adapted to buying or browsing pattern.
The other interesting aspect of predictive analytics applied to sales is that compared to a classic business analytics model, predictive analytics would provide a model that’s constantly changing. As the data evolves, so does the model. This means that your CRM would know your client so well, it would suggest a different sales approach based on the latest behavior, and the history of your client.
This is not new. Predictive analytics has been around for a few years now. Though, the solutions were so complex to use and so heavy to run that it did limit the scope of it’s usage. We now have cloud solutions able to process far more data than before and machine learning solutions starts to really gain traction as Microsoft Azure and Amazon Web Services have a pretty mature product in that domain. The “R” language, open source, is also getting simplified, allowing “less” developpement skills to actually run an algorithm. IBM with their “Cognos” tool, or SAP with their Predictive Analytics Software led the way to what is becoming an avenue of growth. “Data scientist” are now a hard profile to find.
As organisations further improve the efficiency of their CRM usage, predictive analytics will simplify and become accessible through “API ready” software. And as your CRM is becoming a tool that sells, i’m very interested to see how sales organisation will take the shift.
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