Customer Loyalty Analytics

ROI: return of $1.2M per annum (26X ROI) for a group of 40 product, CRM & loyalty managers.

Using AI to provide marketing, product and sales teams with faster, more meaningful insights to support customer/member engagement strategies. Anna allowed different team members to obtain a personalised understanding of current & target members/customers.

Provide analytical insights and detailed Customer strategy development, to create and operationalise a Customer-First cultural transformation.

Suited for brands who want to revolutionise the use of data and science to understand and anticipate Customer needs. Either revitalising an existing Customer strategy or creating a cultural change to differentiate themselves and outperform competitors.

What results can you expect

Studies confirm that Customer-First businesses outperform others, with the top 25% growing:

What questions can it help you answer

Key questions across the business

Dashboards and reports are common tools to report on customers metrics in organisations today. However, due to their rigidity and lack of result interpretation, they can't be used by marketing, sales or product managers to answer key questions across the business.


The dashboard illustrated below is from an ABC company's CRM database. It contains information that are often found in this case including:

  • Customer information: location, age group, gender, loyalty indication, customer value.
  • Business information: store names, store location (post code and suburb)
  • Key KPIs: transaction amount, number of customers
  • Customer View

    Store View

    While we can see a high level summarisation, such as: ABC's total turnover to date: 35.51M customers in age brackets of 25-30 and 30-34 are the 2 most popular segments in ABC's loyalty program, it is difficult for us to get more actionable insights such as which customers I should focus on to grow my business.

    Business users (marketing managers, sales managers, product managers), spend hours or days to drill through each dimension (age group, location, gender, etc.) to understand key patterns.

    Alternatively, they rely heavily on support from analytics teams in which case, waiting for an answer to a question like the above could take up to days or weeks.

    This causes delay in decision making and often businesses make decisions infrequently, on an annual basis instead of pro-actively.

    For day to day decisions, front line teams often fall back on gut feel to avoid this laborious work.

    Summary of value


    Marketing, product & brand managers must build business cases to justify the cost & direction for new product or communication decisions. Before Anna, the process of gathering the required data and insights was estimated to take an average of 5 business days (40 hours) to build an accurate understanding of customer profiles & behaviours per each business case - with business cases often built of quarterly, 6 monthly or annual planning cycles.

    After the implementation of Anna - this was reduced to 2 hours - saving 38 hours per business case (approximately 1 week per business case). Across a team of 40 product & loyalty managers, this equated to a time-saving worth over $1.2M per annum (26X ROI) for a group of 40 managers in the pilot group building a quarterly business case, each additional business cases saving even more cost & time!


    Which specific customers should I focus on to grow my business?

    Scenario 1: Hyper Anna pro-actively suggests customers segments that are experiencing higher/lower growth than what is expected based on historical trend and seasonal effect. The team can then investigate further by asking questions 'what caused this' to understand the underlying cause of the change.

    In this example, Anna suggests us to look at Customers in the age group of 45-49, as in the most recent month, this has experienced a higher than usual growth. Looking further in to 'what caused this', we found that the highest growth among 45-49 has been customers with medium spending in the loyalty program.

    Scenario 2: For my monthly review, I want to understand how different customer age groups have been performing, and from there, identify my highest potential customer group to focus on in the coming month.

    In the example below, we can quickly identify that out of all the age groups, 20-24 is the most important age group for me to double down on as they represents a large volume in turnover and also the highest growth group.

    Further drilling down in this group by customer local council area, we can see that within 20-24, customers from Randwick is growing more compared to others (customers from Randwick represents approx. 15% of turnover).

    Hear it from our customers