This article from our strategy consulting team provides a useful background to the many different ways a business typically uses data.
Identifying these requirements in your organisation is a key first step in developing your data strategy. Use our list as a starting point for your own as you embark on your data strategy journey.
What Do Businesses Use Data Analytics for?
The applications of data are far reaching within a typical business and this article goes into detail on the usage of data within each main area of a typical customer facing business be it B2C or B2B. In general, we can look through an entire value chain of a business and find ways that data will support decision making, create opportunities for sales growth and deliver operational efficiencies.
Here is a summary of the benefits, by function, a business should be looking to achieve through the effective use of data:
Commercial aspects of business
The right range - ensuring the appropriate products are on offer.
Optimal pricing - providing a range of products at the right price levels for each/all customers.
Effective promotions - designing and running promotions that generate the desired incremental sales or product trial.
Rebate optimisation - that encourages share of wallet and purchase weight whilst minimising margin erosion.
Strong supplier relations - developing collaborative marketing and promotional sales campaigns with a businesses' suppliers.
Relevant value proposition - designing a businesses' core propositions to appeal to their specific customer segments.
Effective awareness building - by knowing where to find target customers and how to attract their attention.
Customer satisfaction, retention and loyalty - by understanding customer needs so that the business delivers what they want and keeps them happy.
Increased share of wallet - by ensuring a business is providing all of the right products at the right prices, to drive incremental sales.
Higher conversion - by understanding and optimizing the sales process through reducing friction and making the decision to purchase easier.
Productivity gains (cost to serve) - matching operational resources to customer demand.
Better customer experience - by understanding the purchase decision making cycle for each customer type and designing the experience to suit it.
More repeat business - through understanding the repeat purchase cycle for the customer and proactively reaching out to them at the most relevant time.
Higher conversion - optimizing the online journey through search, product research and selection, to designing frictionless sales and ordering processes to make purchasing as enjoyable and easy as possible.
Improved multi-channel experience - understanding the role of offline and online on the decision making process and integrating the experience across both.
Optimised inventory - predicting customer demand for each product and ensuring sufficient supply is in stock and on the shelves.
Optimised delivery - matching delivery schedules with customer needs to avoid last minute basket abandonment.
Customer centric value proposition innovation - using historic customer purchases and customer needs research to design new products and services that continuously meet your customer needs.
Customer led business strategy - setting out the business strategy in line with the known behaviours and trends of the customer segments and untapped customer groups.
Improved forecasting and understanding of value drivers - using known customer behaviours and metrics to drive accurate financial forecasts and build more effective scenario tools.
Better investment returns - more accurately plan likely outcomes based on customer segments to target investment at areas of highest opportunity.
Improved recruitment outcomes - recruit based on aligning with the customer demand and ensuring that front line staff mirror the customer base.
Improved employee engagement and retention - use data to better measure and understand the drivers of engagement with the business to deliver greater employee performance.
How do different business functions use data?
The following is a series of example uses of data by business function collected over the years by our strategy consulting team, as they develop data strategies for our clients.
How do commercial teams use data?
Strategy: Integrate market and customer insights/trends to support commercial strategy development and monitoring progress to plan.
Ranging Evaluation: Develop customer-based range metrics and reporting to support category management process and customer led product/supplier plans.
Own Brand: Profile of pricing, shopping habits and preferences by customer types to inform, identify and measure own brand opportunities.
Branch Ranges: Profile baskets and missions by customers and stores to identify optimal local range (incl. own brand).
Suppliers: Use range and customer metrics to track and evaluate supplier performance to inform negotiations and open up a supplier collaboration model to optimise sourcing across the network.
Pricing: Track and compare prices against the market to provide benchmarks for price competitiveness, monitor performance and customer impact of price changes to understand optimal levels and sensitivity by customers to drive margin improvement.
Promotions: Use customer performance to identify target clients where a promotion may drive the desired behaviour; track impact of promotions on an ongoing basis by customers, channels and products to inform future strategies and planning and optimisation of the promotional strategy to minimise margin loss while maximising sales.
Rebates: Developing models to understand true profit, optimizing selling, product range and strategies based on existing rebate deals to maximise margin and optimizing future deals based on forecasting models.
How do marketing teams use data?
Strategy & Brand: Track performance of customers habits, behaviours and profitability to enable Marketing to develop and manage the customer value proposition and marketing strategies.
Customer Performance: Track and measure customer profitability, NPS and marketing performance by product, supplier and channels/branches to support marketing prioritisation, decision making and drive ROI.
Media: Inform multi-channel acquisition strategy and planning and track performance of brand, media and marketing spend and their impacts to optimise acquisition rates and media ROI.
Communications Targeting: Reach new and existing customers at the right time at different stages of their journey, through the right channel with relevant messaging to optimise repeat purchase, shorten the conversion cycle, drive upsell and support the overall brand experience (includes all comms as well as customer hub).
Promotions Catalogue: Optimise product selection and placement for newspaper to drive cut through and ROI.
Experience: Track and report NPS to enable the prioritisation and delivery of operational improvements in the customer experience to grow NPS and repeat purchase.
How do sales teams use data?
Prospecting: Use of third-party data to identify new projects and new client requirements. Use historical approval to purchase frequencies to help predict the right sales approach and timeline to drive successful conversion.
Provide sales management with timely insights and root cause analysis for sales performance. E.g. ability to track sales against target by customer type, investigate by product and channel in order to understand the drivers of sales performance.
Enable all layers, down to the individual seller, to track individual performance status, show where they need to focus to meet targets and grow their commission.
Enable all levels to better understand when a financial metric is important or not (e.g. is it significant?). Use analytics to separate out the signal from the noise to help inform where to focus individual and or team effort and drive sales team productivity.
Customer Knowledge: Provide all levels of the sales teams with easy ways to better understand their customers, their likely needs and preferences (e.g. channel, frequency of contact etc). Incorporate this into the strategic sales planning cycle as well as through sales tools (salesforce) in the form of richer profiles to build customer confidence throughout the sales process, increase project conversion and shorten the sales cycle.
Customer Experience and Relationship Management: Enable the sales team to better understand and manage the clients experience, e.g. as well as tracking of key measures such as sales, visits, basket mix to review how these are performing, also include key operational indicators and alerts which might suggest a service issue such as multiple deliveries of the same product.
Recommendations: Use analytics to generate suggestions (and alerts) to boost the capability/capacity of the sales team, e.g. recommendations of preferred brands by client for consumables, through to suggested associated products, higher margin substitutes etc.
How do operation teams use data?
Opening Hours: Use a combination of store/format segmentations, e.g. group stores by the type of customers and missions they serve, local customer profiles and competitor information to identify best fit stores and local opening hour requirements to ensure opening hours are maximising profit and delivering on customer expectations (also potential for cost savings).
Branch Performance: Use multiple data sets to provide an integrated view, by branch, that considers all performance measures together and simplifies the reporting and branch management task for operations. Consolidate performance data across finance, customers, product and employees, to enable a view of true branch performance, root cause analysis and where variances occur that signify improvement opportunities. Drive productivity and performance improvements in branch (time saving).
Store Planning: Understand local catchment, current customer base, shopping mission, share of wallet etc. (develop store profiles) to inform store locations, investment strategies and operational management strategies. Drive return on real estate portfolio.
How do digital and ecommerce teams use data?
Conversion: Enable online customer journey tagging to support the development of standard experience models for different customer types, targeted content placement and product promotion to support the online experience, drive conversion and post purchase activities, e.g. self serve management of orders, delivery changes etc.
Personalisation and Experience: Integrate customer profiles, known preferences and behavioural profiles to key digital interfaces to drive greater customer intimacy and convenience throughout the online experience to drive loyalty to the channel.
Planning: Track customer activity and the associated value across channels to identify where value is being lost to support prioritised product development and increase ROI.
Product: Build out product catalogue and content to enable more online business. (Although, this is not strictly analytics focused, the enriched product data will support better customer segmentations and category management).
Supplier: Enable better integration with supplier systems to share product information and content.
Product Reviews and Feedback: Integrate client feedback and reviews for products to drive buyer confidence and increase conversion and input in satisfaction/experience monitoring, which can then be used to help model loyalty etc.
How do supply chain teams use data?
Inventory: Using customer purchase habits by products and branch, volumetrics and attachments, optimise inventory, space utilisation in DCs (and branches) and reduce stock outs.
OTIF: Identify and profile products most likely to be required for next day delivery so that stock/ranging can be optimised by location to maximise OTIF deliveries.
Delivery: Append customer profiles to key system interfaces and variables, optimise delivery schedules and routes, and understand customer habits to enable customer centric delivery scheduling and optimise driver/delivery productivity.
Driver Performance: Use the Tacometer and Masternauts data to understand driver behaviours, identify opportunities to increase productivity, increase safety and make customer experience improvements.
How do strategy teams use data?
Customer Behavioural Trends: Track customer performance and behaviours across their end to end experience (by channel and location) to understand changes and identify new trends and emerging behaviours to inform operations tactics, strategy and planning.
Product Behavioural Trends: Track the above from a range, supplier and product perspective to inform new category and range opportunities and innovations (e.g. supplier funded marketing collaborations).
How do finance teams use data?
Margin Optimisation: Model clients by profitability, deploy customer driven variables and historical data to understand profitability and cost to serve across the customer experience/channels. Drive margin improvement through removing unprofitable business or introducing new charging models.
Planning and Budgeting: Key customer and channel performance measures for reporting and development of benchmarking and targeting to develop improved business unit and individual plans and forecasts/targets.
Customer performance: Track and measure customer NPM, NPS, performance against credit limit, marketing performance etc. by product and channel to support financial planning and decision making.
Fraud and Stock Leakage: Implement automated models to monitor and alert for fraud and unusual stock movements/patterns.
Debt and Credit Management: Enhance models for providing/extending credit and predicting potential delinquency using look alike modelling. Increase revenue potential and protect margin/loss.
How do HR teams use data?
Employee Profiles: Develop a core set of employee measures/KPIs and segments (incl. diversity, flexible working, attrition, overtime etc.) to understand what drives employee performance. Drive engagement, retention and performance.
Employee Benchmarks: Profile high performing teams/branches and individuals to understand what makes a successful team to shape structures/dynamics across the company and inform hiring/talent strategies. Drive engagement and performance.
Recruitment Strategy: Use employee profiles and develop a retention model to match employee profiles and potential vacancies in order to optimise recruitment strategy and fill vacancies more quickly.
Use of Recruiters: Implement data quality controls over recruitment data and profile recruiters by success against employee profiles to reduce recruiter fees and exclusively engage with high performing recruiters.
If you would like to discover more about how data analytics can help your business, contact our team of experts or learn more from their published analytics insights and business guides, including our detailed guide on How to Use Data Analytics Effectively and How Companies Can Use Big Data in 2021.