Cross-selling is an extremely popular system that involves persuading customers to add an extra, related product to the purchase they are making. Banks, for example, often use cross-selling to sell investment banking services or credit/debit cards to people opening their savings account. Cross-selling is considered to be among the most efficient methods of increasing sales in the competitive environment of today, as it simply is way easier to sell items to an existing customer than a new one.
There are several cross-selling techniques prevalent today, and the next logical product (NLP) or next best offer approach is considered to be among its most effective ones. In this process, analytics are used to help you identify the product that the customer is most likely to purchase next, and then provide them with an appropriately timed offer to make the buy.
The typical process for the NLP involves:
- Consolidating and creating a well-rounded, holistic view of the customers with the help of the growing, expansive amount of transactional and demographic data. This includes information related to customer spending behaviour as well as products the customer already holds.
- Applying predictive analytics on the customer data, and subsequently identifying trends in their purchase behaviours and product affinities. Varied approaches from segmentation, response models or even machine learning techniques can be used to identify these patterns.
- Making use of product affinities/associations for the purpose of recommending other services or products that the customers might be interested in.
- Closing the loop by choosing to apply customer responses to any future targeting processes that address customer needs.
- Predicting the next products that customers are most likely to be interested in would support profitable and long-term customer relationships, in addition to improving customer lifetime value.
Ways to determine the next logical product for your customer
Even though the process of NLP seems to be quite straight forward, determining it can be a bit confusing at times and involves a good deal of effort. Here are some of the ways you can use to identify the next logical product for your customer:
- Based on current holdings: You may determine the NLP on the basis of what a customer has not purchased already if you have a relatively refined set of offerings. For example, an insurance company sells both car and home insurance. An NLP approach will look for all the customers that have invested in car insurance but do not have home insurance, and go on to creating a home insurance offer for them. However, while this is quite a simple approach, it does not take the specific customer requirements into consideration.
- Based on the customer lifecycle: If you offer a product that lends itself, then it is possible that you generate the next logical product recommendations on the basis of the diverse stages of the customer lifecycle with your organization. If you are a software supplier, for example, then there is a chance that your customers go through several stages of need, such as purchase, installation, training, and implementation. You can clearly identify the NLP of the customer in each of these stages, and act accordingly.
- Based on customer life stage: A highly popular way to implement NLP is to try and guess where each customer is at in their overall life stage, and subsequently, offer products that are expected to meet the needs of that stage. The typical life stages you may use would be single, retired, newly married, married with children, empty nesters, and so on. However, the increasingly complex nature of modern customer life stages has made this process a bit complicated. Trends like an increase in the divorce rates and people working long after the official retirement age has made it difficult to reliably implement this approach.
- Based on customer activity: Another good way to implement NLP would be to check out the customer transactions or other activity statistically. This process involves advanced data analytics, as well as a sufficient volume of customer data and transactions. Market Basket Analysis is one of the well-known approaches based on this method. Usually, through the data mining process, you can end up uncovering unusual or unexpected linkages that you may not find through any of the above-mentioned approaches.
- Based on Product and Customer Interactions: Another well-performing though data-heavy approach to recommend the next logical product exploiting Customers interactions with Products previously using Collaborative filtering based methods. It involves identifying the affinities between pairs of Users and Products from historical data and effectively extrapolating them to similar users or products. Such Recommendation Engines are widely adopted today.
NLP aids businesses to develop more attractive, valuable product bundles for the customers and deliver them an increased level of customer service and convenience, while competently cross-selling and up-selling their products. It helps the organization with market differentiation and can help enter new product segments. By opting to focus on NLP, organizations can develop best practices that enhance their customer-centricity. Thus shall invariably aid in increasing their customer satisfaction rate, and positively influence product efficiency and customer yield.