Understanding Customer journeys is challenging - we help you to find the answers and to use the insights
Understanding Customer journeys is challenging - we help you to find the answers and to use the insights
what we can offer you
Identify high-value customers and target audiences
Measure the effectiveness of marketing campaigns and channels
Get deep insights into customer acquisition costs
Helps you increase customer acquisition rates while reducing costs
Identify high-value customers and target audiences
Measure the effectiveness of marketing campaigns and channels
Get deep insights into customer acquisition costs
Helps you increase customer acquisition rates while reducing costs
Multi-touch Attribution
IHC Attribution
IHC is a new type multi-touch attribution model based on the mathematical framework of partial truth, also known as fuzzy logic, and the marketing science concept of customer interaction phases.
Within the customer journey the individual sessions are simultaneously related to each other by timing and engagement depth/ progress.
IHC is fully data-driven – the information you need is fortified constantly with the most current data. Our IHC model learns from every single customer journey, and it gets smarter as it goes.
THREE STAGE CUSTOMER BUYING PROCESS
A three phase interaction concept to model the customer’s buying process has been a pillar of marketing science for 30+ years, our solution brings the approach squarely to the digital age. IHC stands for the three interaction phases:
- Initializer: awareness or attention phase
- Holder: phase of product orientation and keeping the customer’s interest
- Closer: the homestretch, checkout or action phase
Every individual customer journey is projected into the three phases. Hence the attribution results are not only quantifying the general attribution impact of each touch point, but also measuring the strength of campaigns in the respective I-H-C phases as well as cross correlations between different campaigns and interaction phases.
Mixed media modelling
Modelling channel level value contribution
MMM allows us to join online and offline marketing activities, as well as outside effects like seasonality and branding, into an overall model of media spend effectiveness and contribution.
Understand the split of brand effects and its trend
Paid performance marketing channels are mainly evaluated in terms of their direct sales impact, aka. incremental sales. Understanding the behavior of the branded share in sales is at least as important to grow your business.
Key Component 1: Modelling the channel effectiveness over time
Key Component 2: Modelling the channel spend effectiveness / saturation
Ultimately, we want to dynamically optimize the Budget allocation
Uplift & Incrementality Testing
Planned Uplift experiments
Using actual or synthetic generated control groups, we can estimate the incremental uplift of specific treatments, like budget increases or special marketing activities, to customer segments.
Measuring Uplift
The accuracy and strength of any uplift measurement relies on the ability to build a real or synthetic control group. The uplift amount, as well as the delay and the treatment’s saturation effects, can then simply be computed by the performance differences.