The cube consisted of hundreds of thousands of rows on customer-product level spanning across several business units – this meant that prioritisation of the most impactful cases was key. To find and correct the most impactful cases we took the following three-step approach:

• Define the prioritisation metric: The core aim of the project was to reduce incorrect downsell and churn. Therefore, we prioritised customers by their total ARR loss (churn and downsell) over the period of interest.
• Bottom-up approach: We analysed individual customer’s ARR profiles and used supporting evidence to create adjustments to appropriately distribute ARR across the time period of the invoice, removing false ARR loss at the customer level.
• Top-down approach: We then explored customer segments with high levels of ARR loss to identify systematic business logic errors and rectify them across the whole customer segment, eradicating large clusters of false ARR loss.

This methodology allowed us to create a list of suggested adjustments and customer re-mappings to implement, focussing on the first two challenges the client outlined to us.

Auditability was also a key success criteria. To achieve this, we worked closely with the client through a series of workshops where adjustments would be reviewed and accepted. Supporting documentation was prepared through this to justify adjustments and remapping.

Our Approach

The solution required us to leverage both our strategy expertise and analytical modelling capabilities. Our specific activities included:  

1. Demographic Assessment: We leverage publicly available data sets to build a look-alike model, comparing demographics in neighbourhoods to existing, high-value customer segments.
2. Scoring Neighbourhoods: Using demographic and market data, we scored neighbourhoods across various cities to identify which zip code would provide the optimal location to open in.
3. Expansion Blueprint: A tailored rollout plan was proposed, specifying the number of sites per city and a phased timeline for their opening. The plan included a capacity analysis to ensure sustainable growth while leaving room for future expansions.

This approach resulted in an analysis tool displaying heatmaps of key statistics within US cities which highlighted optimal locations to open up in. This tool also identified potential cannibalisation risks as the expansion progressed.

To overcome these challenges, we built a self-serve data platform for the client which would support our client in increasing their planning horizon. We leveraged our data capability and reporting experience by focussing on the following activities:

1. Understanding the Client’s Needs
   We began with engaging the C-suite, finance, and sales operational leads to understand their pain points. These pain points were collated and used to design the reporting tool.
2. Data Assessment and Process Recommendations
   Our team evaluated the level of detail available within the client’s existing data systems to perform a feasibility assessment of the reporting tool design. Where data was unavailable, process improvements were outlined to enable more granular reporting in the future.
3. Reconciliation and Data Quality Improvement
   A rigorous reconciliation process resulted in the creation of a clean customer ARR cube. This process corrected historic manual errors, particularly in invoice dates, and significantly improved data quality.

The first phase of work involved understanding the budgeting model in detail – this was critical to be able to get to true margin for items. This involved:

• Working with Heads of Finance, B&M and Marketing to understand all the moving pieces which were affecting true margin
• Categorised costs into fixed and market-affected, to understand the levers that needed to be flexible for budgeting
• Used the components to build a budgeting & forecasting tool for the financial margin, including back margin at the item level

Using the budgeting tool, we then identified that a complex supplier base and promotional strategy was eroding margin in key categories. In particular:

• Fulfilment costs varied across categories and needed to be factored into discount decisions
• Promotional activity mixed between being supported by suppliers and not, affecting margin
• Using a mixture of targeted discounts and promotions kills margin quickly

Through this, we devised and implemented an updated markdown strategy to protect margin whilst holding volume.

To uncover the root causes, we used a hypothesis-driven performance diagnostic. We worked closely with members of the key business functions to collate a series of hypotheses. We then assessed the relative importance of these hypotheses using various techniques:

Market Modelling

• We built market models for both the retail and wholesale arms of the business to enable us to assess performance relative to market
• Key variables, such as the rise of second hand and government legislation, were assessed to understand their impact on the current and potential future market
• Used a combination of competitor research, expert interviews and consumer surveys to calculate triangulation points

Internal Analysis

• We used our analytics expertise to aggregate disparate data systems across all acquisitions, enabling us to understand organic and inorganic performance
• Analysing sales performance across product and customer segments allowed us to understand the key areas which our client were winning and losing in

Competitor Benchmarking

• We analysed customer sentiment for our client and their competitors to understand how service challenges had changed customer preferences
• Conducted a price benchmarking exercise to understand the product types and regions where price competitiveness had risen

The combination of these methodologies allowed us to formulate a view on our clients performance versus market. More importantly, testing hypotheses meant that the key drivers could be assessed for their strength of impact on the performance.