Sales Performance Analytics
(Retail & FMCG)
ROI: return of $1.06m per annum (21X ROI) for a group of 50 brand, category and marketing managers.
Using AI to provide retailers & FMCG/CPG companies with faster, more meaningful insights to support category, range & space decisions. Anna allowed teams to obtain a personalised understanding of their current & target customers, with the ability to deep dive across various areas - such as product/category performance (down to SKU level), store/channel preferences (e.g. location, online/offline), brand loyalty, NPD performance & customer segments.
Problems we were tasked to solve and audience
Helping a multi-national consumer goods company to:
Understand your performance like never before
Optimise your range
Optimise your channel strategy
Enhance your promotional strategy
Frequently used terms:
Existing process to access insights was painful
Result delivered (sample questions)
WHAT HAS HAPPENED
Summary of value
Brand, Marketing and Category managers create weekly performance reports and hold a weekly meeting to review performance and plan ahead. Before Anna, the process of gathering the required data and insights was estimated to take an average of 4-6 hours. After the implementation of Anna - this was reduced to 30 minutes. This equated to a time-saving worth over $1.06m per annum (21X ROI) for a group of 50 business users.
Tasks such as creating your own reports or dashboards require the knowledge of where to navigate through your data for insights, but this has been proven difficult and time consuming.
Using the power of AI, Hyper Anna executes millions of hypotheses to discover complex patterns in datasets. The most practical and relevant insights are then pro-actively delivered to your inbox. In contrast to large teams of analysts finding answers from data, Hyper Anna will single- handily do it in a matter of hours.
In this example below, Hyper Anna automatically suggests a planning manager to look at categories that are underperforming significantly.
Getting insights from data is now as simple as asking a question.
In this example below, a category manager has asked Anna to analyse the store performance by department.