ROI and Customer Value Management
As a marketer or program manager you will be responsible for creating customer value. This may be customer value (such as engagement or service), or business value (for example, increase in sales). Usually these two go hand in hand. Since a customer who is not satisfied, spends little money or will leave your company altogether. Conversely, you can invest significant budgets to retain the customer and keep him satisfied, creating the risk of the retention costs being higher than the generated revenue. Customer Value Management (CVM) is the harmony between a customers perceived added value and that customer’s net value for your business. Aligning your marketing strategy in such a way that you achieve a maximum ROI.
Let’s get more specific. In the above Customer Value Management cycle you are responsible for attracting new customers and retaining these customers. For the latter segment, you develop a retention programme. Your focus is on retaining customers, who may at certain times in the customer life cycle consider leaving. While developing your program, you have a number of resources at your disposal. For instance choosing the channel with which you approach your customers; where you might consider a more expensive, but more personal and effective channel such as telemarketing, or a less expensive channel such as email. Subsequently, you still have the content message you have to focus on: ask your customers if they want to renew their contract, offer them a promotion or discount, or say nothing?
Now, the choice is yours. Do you grab your shotgun and approach all your customers in the same way. Or do you use your channels and propositions, optimising them as much as possible for an optimal Customer Value. Obviously you strive for the latter, but how do you determine what the most efficient and effective deployment of your resources is within a given program? With the use of data and statistical models!
Customer Data: Get to know your customers (again)
In todays’ complex world with an up-scaling of activities and E-commerce it is increasingly more difficult to keep track of customers and their individual preferences. Whereas previously most customer insights were generated by personal interactions with the store owner or sales representative. In that retrospect the customer has become more remote and it is increasingly challenging to close the growing gap.
However there is good news. With the advent of big data there is a rise in customer insights again. By combining large amounts of data from various systems, you can create a 360-degree customer view. Using increasingly sophisticated machine learning algorithms, you once more have the possibility to get to know your customer again. Based on his/her behaviour and characteristics, you can determine what products he/she needs, but also be able to predict the risks of them cancelling a subscription. By using information hidden in the data, you can become a true data sniper.
With Oxyma’s Insights department we assist companies with their data analysis requirements in order to help them increase Customer Value and their customer Value programs. Here are a couple of examples how predictive data analysis can work and the impact it generates on your business.
Predictive data modelling cases: A double hit
Case: Decreasing Churn
Who: a telecommunications company
Question: In a campaign designed to retain customers, everyone with an expiring contract was contacted. The campaign had the opposite effect than hoped for. Dormant customers were activated and as a result, these customers now became aware of the option of cancelling their subscription. The shotgun method ended up leading to more terminations. The imminent question arose: how do we identify potential clients without awakening the inactive customers?
Solution: A machine learning algorithm determined what the churn risk was for each customer. Combining the churn risk of a customer and their customer value, the customer base was split into groups; each group could have its own unique marketing campaigns. With the model in place, it allowed us to identify and approach only those customers that were high risk and high value while keeping the low risk sleepers out of the campaign.
Result: A statistical data model pin pointed up-to 5 times more accurately which customers were most likely to terminate a subscription, than a random approach. Allowing to save budget by using very specific targeting and increasing customer value by extending the customers life time.
Case: Increase conversion – Next Best Offer
Who: Marlies|Dekkers, an exclusive lingerie brand
Question: How do you achieve maximum results with a limited marketing budget?
Solution: Be relevant to the customer and personalise your message. Based on behavioural and personal information, and a next-best-offer, an algorithm is determined on the preference of the customer. Instead of an email with a generic brand/product message, the client receives a customised email containing, his/her preferred product according to the algorithm.
Result: With personalised campaigns, the conversion rate increased by 60% which in turn generated a significant increase in ROI.
Become a data sniper too
Are you also involved with Customer Value Management? Then don’t just shoot around, but use the power of data. Connect all the available information on prospects and customers, run the relevant algorithms on the data and subsequently determine a targeted marketing tactic for each customer (group). Be a sharp shooter and create customer value.