Why sales teams should harness retail analytics for true strategic precision

9 May 2022

Why sales teams should harness retail analytics for true strategic precision

Are sales teams able to successfully deploy margin-securing strategies in an exceedingly fast-paced and competitive industry? 

This is the critical question, more important than ever as the retail market becomes more saturated, online shopping experiences grow increasingly sophisticated, and the margin for error within sales processes draws finer and finer. 

For these teams, retail analytics promises a wealth of advantages that can transform the strategy and execution of sales processes. It can guide you to reach the widest number of potential prospects, before providing insights on how to successfully nurture them to secure a conversion.  

From greater accuracy to a higher level of trust in strategic decisions – including the vital buy-in from key stakeholders – we’re looking at the core benefits of introducing retail analytics processes to your sales teams today. Read on to learn more.  

What is retail analytics? 

Encapsulating a wide range of analytical and data-centric processes – from data visualisation to prescriptive analytics – retail analytics can be used throughout a retail business to provide a data-driven approach to insights.  

As a result, components of a retail enterprise, such as customer management or marketing, become based on targeted data and the latest trends, rather than general market research or gut feeling. Making use of these insights gives teams a greater level of accuracy and reliability in their campaigns and activities across the enterprise. 

Just some examples of retail analytics include:  

  • Identifying the overarching trends across customers and regions with Big Data analysis 
  • Visualising the performance of products, premises, and promotion in accessible dashboards 
  • Forecasting the success rates of promotional campaigns to assess risk  
  • Creating automated processes for monitoring inventory levels to protect margins  

For sales teams, retail analytics delivers an enhanced level of trust in datasets for promotional or sales activity. What’s more, it can also bring a wider range of value for unprecedented precision in results. 

Sales, retail analytics, and the importance of accuracy 

All data must first be structured and stored in a centralised platform like a data lake or data warehouse. For a sales team, the importance of this cannot be understated. A well-engineered database provides huge value before you even begin to perform any retail analytics processes.  

With access to unfaltering and trusted intelligence, teams can more easily secure the buy-in of vital stakeholders. It also encourages a more collaborative, data-driven, and centralised environment that reduces the creation of harmful data silos.  

Predictive analytics in retail: the core benefits  

For sales teams wishing to take a more comprehensive and strategic approach to their campaigns, predictive analytics processes are capable of providing previously unattainable intelligence. While the initial technical demands can be a barrier for all but the most sophisticated retailers, predictive analytics takes advantage of machine learning modelling to forecast the potential outcomes of sales-related decisions, such as:  

  • The outcomes of promotional activity and the effect on margins 
  • The likelihood of profit when products are priced at differing amounts 
  • The effect of seasonality on buying habits  

With insights capable of instructing campaigns, it’s easy to understand how valuable predictive analytics are to sales teams.  

What’s more, once these results have been fully realised, they can be used as the foundation for prescriptive analytics for truly empowered intelligence.  

Read more about predictive and prescriptive analytics in our guide.

What should teams using retail analytics need to consider? 

For sales teams looking to leverage the full potential of retail analytics, some core demands must be addressed to optimise your results. Two of these are: 

Encouraging a data-driven culture 

Encouraging a data-driven culture across an enterprise ensures that the value of data is properly communicated, preserved, and governed. What’s more, with users fully aware of how data should be stored and interacted with, it’s possible to mitigate the risk of harmful data silos being created – with teams choosing instead to collaboratively access securely collated data. 

Consistent reporting processes 

While accessing reports created through retail analytics can present a wealth of benefits for any sales team, it’s important to utilise them correctly. One significant issue to be aware of when creating time-sensitive reports is the need to access them at the same time consistently. When not observed, this has the potential to omit vital data, causing unnecessary bias and skewing results – which may have disastrous consequences.  

Learn more about these consequences and the importance of not omitting data here. 

What’s the future of retail analytics within sales? 

As the world of analytics continues to evolve rapidly to meet increasing demands, we’re eager to discover what the future of retail analytics holds for sales teams wishing to take advantage of greater capabilities.  

One aspect that we expect to see more innovation in is prescriptive forecasting. As more and more teams realise the benefits of proactive, future-facing analytics, we expect to see an increase in demand and sophistication as analysts seek greater capabilities to stay ahead of their competition and reach critical areas of profits first.  

We also expect to see these tools grow increasingly user-friendly and accessible to teams of any skill level, prioritising the user experience to empower teams like never before.  

Still curious about how analytics can empower company-wide teams within the retail industry? Download our complete guide to learn how to boost your margins with retail analytics today. 

Free download: 5 ways to boost your margins with retail analytics

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