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

  • Identify the key metrics that are driving performance - across categories, brands, SKUs & competitor ranges
  • Understand how performance is shifting over time
  • Optimise your range

  • Identify your best and worst performing products within your range across a variety of performance metrics
  • Measure the performance of recent product launches
  • Optimise your channel strategy

  • Measure growth across categories, brands & SKUs
  • Understand product performance within & across specific retailers & channels
  • Enhance your promotional strategy

  • Understand how promotions drive performance
  • Understand how promotions have performed
  • Frequently used terms:

  • FMCG, CPG, Retail
  • Category management
  • Range optimisation / delisting decisions
  • Promotions / promotion optimisation
  • Range & space decisions
  • Customer profiling
  • Audience

  • Category Managers / Buyers / Category Management teams
  • Brand Managers / Brand Management teams
  • Marketing, Promotion, Trade & NPD teams
  • Range, Space & Display teams
  • FMCG / CPG / Retail executives
  • Data

  • 2 years of sales scan data covering 5+ categories, all brands & SKUs, across 10+ retailers
  • Nielsen competitor sales data across all categories
  • Ability to drill down including brand/SKU/categories & channel/postcode/store format
  • Existing process to access insights was painful

  • Information about channels and retailers is often collected and stored in different formats with different levels of detail.
  • They are often manipulated in tools like Excel with manual effort to be able to answer only some specific questions that are well known.
  • The ability to ask ad-hoc questions is resource-intensive and prone to error.
  • This results in lost opportunities for the creation of highly targeted and effective marketing solutions and retailer fine-tuning.
  • Result delivered (sample questions)

    Sample Questions


  • 1.“I need an overview of our sales performance, across our brands and our retailers”
  • 2."Trend of value sales for Personal Wash category by brand"
  • 3."Quantity and sales for Personal Wash category over time"

  • 1.“What has happened with our sales that I should take a deeper look at?”

  • 1.“What are our best and worst growing channels?”
  • 2."Show me top 20 products sold in 50% off campaigns last month"
  • 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.

  • With Anna, users are able to understand not only the size and shape of performance across retailers, channels, and product cuts from SKUs to brands, but they are able to ask any data-related question that comes to them and get an answer in seconds.
  • Allows users to craft highly-targeted marketing campaigns and retailer optimisation works which maximise the potential of each channel and retailer.
  • Customer profiles across product cuts and retailers and channels are no longer a mystery.  Management is able to quickly understand performance across each profile and to find opportunities for improvement.
  • Performance anomalies are presented to Management proactively, saving precious time looking through and understanding data points, allowing Management to arrive at conclusions better and faster.
  • Screenshots

    Pro-active insights

    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.

    Ask anything

    Getting insights from data is now as simple as asking a question.

  • Surpass the “create vs. consume” dichotomy of reports and dashboards by giving all users access to a self serve platform
  • Govern your data centrally but democratise access to all
  • In this example below, a category manager has asked Anna to analyse the store performance by department.

    Hear it from our customers