Customer Relationship Management has come a long way in the last 10 years. An evolution has taken place. Although this is different from company to company, the general path that has been adopted from company to company typically has followed:
1. Formal Implementation of CRM System
Organizing customer data formally and facilitating a view to various stages of customer lifecycle. This also creates a crucial bridge between sales and marketing. Support systems such as ESP (email service providers) should also be implemented at this time
2. Customer Onboarding Program
Automated Email workflows that are triggered by online (or depending on sophistication, multi-channel) activities, including newsletter subscriptions, initial purchase, product inquiries over web or phone, etc.
3. Creation & Deployment of Loyalty Program
Creation of a points based system that encourages and rewards engagement, retention and loyalty to the business.
The setup of a Loyalty program may come before or after formalization of a digital customer onboarding program. In many ways, it’s beneficial to have the loyatly program in place first to prevent necessity of backtracking on many customers.
Loyalty programs should be focused on the following principal targets:
- Increase in engagement between customer and business (driving revenue)
- Increase in share of basket with the customer
- Increase in retention and brand stickiness with customers
- *Auxiliary revenue streams
*Auxillary revenue stream is true for businesses with paid or monetarily tiered loyalty programs, but not necessarily applicable to all businesses.
4. Customer Segmentation Modelling
Organizing customers into groups bound by key indicators. These indicators are often specific to company value drivers, but may be spread across a range of engagement tactics. Good practice is to tie the key indicators to margin generating activities.
For a long while, 4 stages has been the key final step in setting up a customer loyalty program,, and tying it to supporting digital marketing and wider business tools in place to support engagement goals and incentivization.
The Rise of the Machines!
In the last few years, the rapid rise of big data analysis, machine learning, and neural networks has created a paradigm shift in how many businesses look to segment their audiences. This has led to an additional variable in approaching customer segmentation and targeting.
5. Dynamic Customer Affiliation & Lifecycle Modeling
Leveraging data science, machine learning and AI to create predictive modeling that connects the most susceptible and value driving customers to the goals that are intended by the business.
The Forth and Fifth phases of Customer Relationship Management are, in my opinion, where cracks can begin to appear between the actual needs and wants of customers, and the perceived value addition activities for the business in question. Machine learning derived analytics are certainly a more sophisticated method of segmenting and targeting than traditional customer segmentation modeling, as this will likely include a wider variety of multi-variates in determining audience. However, this methodology is still far from infallible, and we are still at the early stages of this new science.
The key differentiation that must be taken into account when looking at manual or dynamic customer modeling is to marry INTENT to BEHAVIOR.
Here is an example of the challenge presented by both traditional and machine learning driven segmentation modelling:
- Customer A frequently purchases a large variety of products from a department store chain both online and in-store but never buys the category of electronics or electrical items.
Segmentation logic in either 4 or 5 will likely look at this customer and determine there is an opportunity to expand the sales channel of the individual to broaden their basket to drive purchase of electronics. However, there could be a wide variety of factors that influence why the customer does not purchase electronics from this particular retailer.
- Lacking technical savvy to make informed purchase decision
- More heightened sensitivity to price – preference to research and shop around online for best deal
- Preference to specialty retail for this category
None of this information can be easily gleamed by #4 or #5 in our customer segmentation modeling. What is more likely to happen is that an opportunity for a segment of customers who COULD buy electronics, but are NOT buying electronics will be formulated, and marketing messaging will be disseminated to these customers across multiple channels that may convert some. The downside is that this approach will likely alienate many customers who re not purchasing this category for any of the reasons above from the brand, and ultimately decrease their engagement.
The introduction of a Sixth Phase to the approach to customer loyalty and CRM is now imperative. To get true insights to drive customer targeting and accurate, relevant, value driving messaging, a partnership with sales and customer service teams is required. The gathering of useful data that can be cataloged on the customer, and used to actively adjust the known profile of that customer is now key.
6. Refined Customer Preference Modeling
Capturing and utilizing customer interactions to infer preferences and interests, and link this back to machine learning driven segmentation rules.
This is the next logical step in the evolution of digital customer experience. Customers provide their sentiment, and this is both negatively and positively utilized to drive targeting and management of affiliation to campaigns and segmentation models. Companies are bringing forward technology that can help to digitally capture such preferences.
A great example of this is Store Advise, an innovative retail associate platform that can gather real-time insights to customer preferences during interactions. I believe we will see more and more of this through facilitated services and guided conversations both in dynamic interactions, and through point of sale interactions during both retail and B2B engagements. The key is capturing the intent & desire, and communicating it in a format that can be easily interpreted by the internal company systems and converted to actionable insights.
Such behavioral triggers have been embedded in social media for some time, with the ability to curate the messaging order and frequency that is displayed in a feed through liking, disliking and blocking certain messages as they appear. Although this practice has been criticized in the social media space for creating ‘Echo Chambers’, in the majority of B2C interactions, more accurate and relevant targeting that takes into account preferences explicitly voiced by the customer, can be a positive differentiator.
As transparency to customer actions becomes more and more visible through big data and increasing digital tracking, combined with the stronger associated data assortments on the product side, businesses should take a critical view and make a determination of what additional customer insights can be leveraged en-masse to drive messaging targeting to ensure that customers remain loyal, informed and that the communications being provided are being done so with the customers’ wants in mind, not just the business revenue drivers.