How We Increased Campaign Engagement by 50%

Marketing teams used a complicated process for prioritising offers across insurance, travel and automotive verticals. This limited cross-sell opportunities, such as introducing insurance to an existing roadside-assistance member. It also made upselling, such as recommending a higher membership tier, harder to personalise.

Another concern was that member information existed across multiple systems, including purchase history, behaviour and demographics. However, marketers had to analyse this information, build audience groups and manually match offers. These groups also followed fixed rules, such as age or past purchases.

These did not adapt to changing member behaviour or intent. Teams struggled to identify which additional service or upgrade was relevant, and when.

Creating more personalised campaigns meant repeating this complex workflow, adding workload as campaigns scaled.

The organisation partnered with Techolution to automate campaign planning using Full Enterprise Automation (FEA).

We built the Decision Advantage Engine to recommend audiences and offers across businesses using member or customer data and campaign results.

Marketers start by importing an existing campaign into the Decision Advantage platform. They define the campaign goal, the target result and how personalised the messaging should be.

It then recommends the next suitable action for each member, including cross-sell or upsell offers. The system also recommends when to deliver each offer and through which channel, such as email or web. Additionally, the system generates personalised subject lines and email copy for each audience.

Our BPA 4.0 framework helps us build this solution. Its Cognitive Decisioning capability shows why each audience, offer, and content option was recommended. Marketers now see which member signals influenced the decision and trace claims to source data, campaign goals and approved business rules.

The system also ranks content alternatives, explaining their strengths and where they fell short. This makes the reasoning, supporting evidence and gaps visible for marketers’ review before launch. Instant Learning (On-The-Job) uses campaign outcomes, member interactions and marketer feedback to improve future recommendations. This supports ongoing campaign optimisation without separate retraining cycles.

The impact appears in both campaign performance and marketing capacity. Better audience and offer decisions also create wider business value:

The result is not simply faster campaign planning. It is a repeatable way to turn member data into better decisions across every business line.