Here's some tips on how to make it happen, including how real-time retail analytics matter more than anything in today's more challenging retail...
4 Types of Retail Analytics Every Business Needs in 2018
4 types of retail analytics every business needs in 2018 for DSD automation and improve operational efficiency.
Both Retail analytics and DSD software have come a long way in the last few years. If you wanted to see what your customers thought about your store or why they come there, you would need to ask them directly by conducting some form of market research. You would need to conduct surveys in person or via email, or you would have to wait until long after to give them a call and ask them how it went.
Gone are the days where you would have to stop your customer. You would have to talk to them in the middle of their buying process to see what they are thinking and how they are feeling about their shopping experience. Stopping them would mean that you interrupt the buying process and may even affect the outcome of what the person buys in the end.
1. Descriptive analytics:
Descriptive analytics gives retailers a summary of the performance of the bulk of business actions – think transactional history, inventory changes, promotional success and so on. This type of retail analytics isn’t new. Retailers have used descriptive analytics to analyze direct mail campaigns to determine response rates, costs per lead and conversion rates. However, descriptive analytics has taken on a new form with the advent of Big Data. Retailers using website tracking data can determine how many users visited a site, the pages they saw, the time they spent, the links they clicked, the links that led to acquisitions, and so on.
2. Diagnostic analytics:
Diagnostic analytics, like descriptive analytics, also looks at past performance, but diagnostic analytics adds context to data to discover trends or causal relationships between variables and outcomes. Diagnostic analytics provide probabilistic insight into ‘why’ outcomes resulted as they did.
3. Predictive analytics:
Predictive analytics enables retailers to anticipate trends and shopper behavior based on the historical relationships between variables as discovered by diagnostic analytics. This form of DSD software analytics applies various statistical methods, such as machine learning and data mining, to bodies of data to forecast trend lines. There’s always an element of uncertainty with predictions, and predictive analytics is no exception – a manager must verify with their analysts the source of the data, whether the data is representative of their customers, if there are outliers in the data, which assumptions are in the data, and what conditions would need to arise to render those assumptions invalid.
4. Prescriptive Analytics:
Prescriptive analytics allows retailers to make incremental adjustments in anticipation of changes in consumer sentiment, demand, supply shocks and so on. For example, movie theaters, airlines and cruise ships can change ticket prices to accommodate anticipated changes in demand. Recommendations come in real-time – by the day or by the hour.
iControl’s collaborative DSD analytics solution offers exactly this. It provides a normalized, harmonized, and secure web portal where critical trading information can be shared. iControl helps you to analyze product orders and allows retailers and trading partners to proactively evaluate past in-stock performance. We also provide forecasted inventory levels with alerts that anticipate expected out-of-stocks among our DSD Solutions to maximize profits in 2018.
To learn how the iControl suite of solutions can help your business make better merchandising decisions, contact us today.