Retail analytics focuses on providing insights related to sales, inventory, customers, and other important aspects crucial for merchant decision-making processes. The discipline encompasses several granular fields to create a broad picture of a retail business’ health, and sales alongside overall areas for improvement and reinforcement. Essentially, retail analytics is used to help make better choices, run businesses more efficiently, and deliver improved customer service analytics.
The business impacts of these analytical tools are real. According to a recent study conducted by IBM’s Institute for Business Value, 62% of retailers report that using retail analytics has created a valuable competitive advantage for their organisations.
The field of retail analysis goes beyond superficial data analysis, using techniques like data mining and data discovery to sanitise datasets to produce actionable insights. Moreover, companies use this data to create better snapshots of their target demographics. By harnessing sales data analyses, retailers can identify their ideal customers according to diverse categories such as age, preferences, buying patterns, location, and more.
Read on to find out more about how retail analytics truly is changing the game and giving companies the competitive edge they need to stay afloat in this everchanging competitive retail landscape.
Customer behaviour analytics for retail
Deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalising campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs. Consumers today interact with companies through multiple interaction points via mobile, social media, stores, e-commerce sites and more – making it hard to keep your finger on the pulse of the people manually. This dramatically increases the complexity and variety of data types you have to aggregate and analyse.
When all of this data is aggregated and analysed together, it can yield insights you never had before — for example, who are your high-value customers? What motivates them to buy more? How do they behave? How and when is the best time reach them? Armed with these insights, you can improve customer acquisition and drive customer loyalty.
Increasing conversion rates through predictive analytics and targeted promotions
To increase customer acquisition and lower costs, retail companies need to target customer promotions effectively. This requires having a 360-degree view of customers and prospects that is as accurate as possible.
Historically, customer information has been limited to demographic data collected during sales transactions. But today, customers interact more than they transact – and those interactions occur on social media and through many other channels. Because of these trends, it’s in the best interest of retailers to turn the data customers generate via interactions into a wealth of deeper customer information and insights.
Data engineering is capable of correlating customer purchase histories and profile information, as well as behaviour on social media sites. Correlations can often reveal unexpected insights — for example, let’s say a few of a retailer’s high-value customers “liked” watching the Food Channel on television and shopped frequently at Whole Foods. The retailer can then use these insights to target their advertisements by placing ads and special promotions on cooking-related TV shows, Facebook pages and in organic grocery stores. The result? The retailer is likely to encounter much higher conversion rates and a notable reduction in customer acquisition costs.
Operational analytics and supply chain analysis
Faster product life cycles and ever-complex operations cause retailers to use big data analytics to understand supply chains and product distribution to reduce costs. Many retailers know all too well the intense pressure to optimise asset utilisation, budgets, performance and service quality. It’s essential to gaining a competitive edge and driving better business performance.
The key to utilising data engineering platforms to increase operational efficiency is to use them to unlock insights buried in log, sensor and machine data. These insights include information about trends and patterns that can improve decisions, drive better operations performance and save millions of dollars.
Servers, plant machinery, customer-owned appliances, cell towers, energy grid infrastructure and even product logs — these are all examples of assets that generate valuable data. Collecting, preparing and analysing this fragmented data is no small task. The data volumes can double every few months, and the data itself is complex.
Data engineering allows you to quickly combine structured data and combine them with unstructured data. Then, utilising the right analytical tools, you can use this data to further make it effective.
For more information on how retail analytics supercharges businesses, visit our site.
When it comes to data analytics, the retail space has emerged as a major segment that is heavily leveraging SAS software. This is especially true when it comes to omnichannel retail analytics with SAS software – why wouldn’t it? A recent study by the CMO Council found that 54% of consumers would consider ending their relationship with a retailer if they are not given the tailor-made, relevant content, and offers that only omnichannel retail analytics could provide.
With such an emphasis on analytics in the retail space, it goes without saying that leveraging the right platform is critical. That’s precisely what this blog dives into. However, before we move any further if you’re uncertain as to what omnichannel retail analytics is, here’s a brief overview.
For many years, omnichannel analytics has been all about bridging the divide between digital and physical channels in an effort to identify customers and precisely where they are based/located. What devices do they use? What platforms? Do they use mobile apps? What time of day do they shop? What products do they like? Do they shop on social media? These are just a few insights omnichannel retail analytics can provide.
Omnichannel retail analytics with SAS software, in particular, can play a driving role in giving businesses insights into their retail customer’s purchasing journey. With all these insights, business would have access to a wealth of information and the power to market products to customers at the right time and on the right platform, and channel.
Now that we’ve run through a basic overview of omnichannel retail analytics, here are three reasons SAS software is ahead of the pack.
Ten years ago, very few people would have predicted the impact social media and the evolution of mobile devices have had on global commerce. With customers stepping into the mobile space exponentially every single day, the opportunities are endless.
However, given how crowded the online space is with both consumers and competitors, making sure you have the right insights to drive revenue and generate interest is critical. That’s where retail analytics comes into play.
Whether you’re focusing on Facebook, Instagram, and just about any other social platform, or you’re looking for more information on how your brand is being utilised across a variety of devices, SAS software has the ability to deliver these insights with ease.
In a way, the move to multiple channels and platforms has made things much difficult for retailers. Instead of focusing exclusively on one mode of demand generation and sales, they now have to focus on multiple avenues. The good thing about this? More channels = more opportunities (provided you use the right tools and platforms).
With SAS software, retailers can leverage retail analytics with ease and match the speeds and convenience at which customers research products and compare prices across multiple channels. Thanks to this speed, they now have the ability to respond with relevant offers, competitive prices, and the products their customers and prospects are most interested in. A little personalisation goes a long way!
Many retailers operate multiple sites across vast geographical areas, which means that while the overall brand is the same, the target customer segments can vary significantly. Gauging these insights and information on the ground and at the store-level can be very difficult without the help of a substantial analytics platform.
With SAS software’s omnichannel trade area analytics, retailers can now understand customers in specific communities and regions, across multiple channels. This gives businesses and store managers the ability to tailor customised messaging, pricing, sales, and marketing efforts down to the store level.
Unlike many analytics providers, SAS has consistently managed to stay on par with the latest trends and developments, enabling it to ensure that it’s users and customers have access to the most up-to-date insights in real-time. As the retail space continues to evolve, so will retail analytics and you can rest assured knowing that SAS software will as well.
With many years of experience as a SAS partner, in addition to countless years of shared SAS experience among our team members, our team here at Selerity have allowed many of our clients to enjoy significant savings – sometimes in the range of millions of dollars. If you would like to know more about our SAS services, our company, and our work with SAS, in addition to how you can leverage our expertise, feel free to reach out to us, or stay tuned to this feed.