Decisioning is a data-driven process for automatically selecting the most appropriate message, content or offer for a customer in real-time.
There are many similar terms for Decisioning, including:
- Real-Time Interaction Management (RTIM)
- Real-Time Personalisation
- Next Best Action
- Next Best Offer
Regardless of the term used, the objective is always the same: put the most relevant thing in front of the customer, at the most relevant time, automatically.
Advances in analytics and marketing technology enable this decision-making to happen in a split-second, based on each customer's own unique data profile.
Some real-world examples of decisioning include:
- Promoting the right product to cross-sell to each customer via a personalised banner on your website
- Promoting complementary products based on your customer's last purchase via a personalised email campaign
- Recommender models that suggest the next video a customer should watch
- Recommender models that show other related products you may be interested in
- Spotting a service issue and proactively presenting a customer service message
- Identifying vulnerable customers and promoting a message to fast-track support
In all of these cases, the decisioning engine needs to make a choice over what to show customers, often from hundreds or thousands of possible options. Data science plays a key role in this process, using techniques such as predictive and adaptive modelling to understand and balance the relative probabilities of customers taking action, the value to the business of a conversion, and other relative priority weightings.
Decisioning frameworks are incredibly powerful for brands, particularly where they have a large product portfolio and want to promote only the 'right' products without bombarding customers with irrelevant offers.
When successfully implemented, decisioning frameworks allow brands to be:
- Personalised, with tailored messaging for every customer
- Consistent across channels, maximising the chances of conversion
- Agile, by responding in real-time to new customer data points
- Empathetic, by accounting for customer context
