POS data in the retail industry

Point of sale data measures how much product the end users purchase. It provides the

sales performance of the product that a manufacturer makes. This data is available directly from the end-user. But when a company’s business is two or more steps away from the end-user, it is very likely that POS data no longer represents the sales of their products. The data

still would improve the company’s demand forecast though.

No matter how far away you are from the end-user, as long as your product is part of the inputs to make the final product, POS data is always related to the sales of your product and can be used to improve your demand forecast.

Brands are utilizing POS data in different ways to help improve sales. Surveys indicate

that brands are most interested in leveraging POS to improve planning and forecasting,

followed by gaining better visibility into new product launches, enhancing promotions

and reducing out of stocks. Since vendors have the POS data available to them; it now

becomes their duty to provide the insights to other brands in the product cycle so that in

the end, the POS data is useful for the sales of a product as a whole.

Following are the ways in which POS data can be leveraged by vendors:

Buyer meetings: Buyers need help from vendors with respect to sales and margin

growth and more so in today’s competitive retail environment. Vendors need to

convert the raw POS data into valuable insights that help buyers accomplish their

tasks i.e., maximize sales, optimize promotions and improve margins.

 Retail category: The Retail category is a strong leading indicator of a vendor’s

willingness to come to buyer meetings armed with data. Though it proves to be an

important indicator that drives insights, survey shows that in an average vendors

from Housewares (84%), Food & Beverage (78%) and Beauty & Cosmetics (74%)

all indicate a proactive approach to presenting insights from POS in their buyer

meetings, while those in Electronics (44%) and Baby & Children (44%) appear to

be less proactive in leveraging data for buyer meetings. Therefore, vendors in the

electronics and baby & children sectors need to quickly become proactive in

converting the raw POS data to valuable insights.

Culture: It has been seen that vendor’s who regularly use data in decision making

across the company are more likely to provide POS insights to buyers in

comparison to vendors of other brands that aren’t leveraging data on a regular

basis internally. Therefore, the vendor’s culture is one of the deciding factors on

whether or not a brand will bring the data into buyer meetings.

Since, buyers are risk averse and deal directly with the product marketing, by providing

valuable insights derived from POS data will help such buyers to understand the

product, competition and industry helping them improve their comfort, confidence and

therefore increase the speed of their decisions.

Following are the ways in which POS data helps teams to optimize their operations etc.

Mobile: Similar to the mobility offered to customers on checking product details

in their mobile devices, today’s brands are increasingly harnessing mobile

technology to drive their in-store execution. It could be optimized further with

the addition of real-time POS and inventory data that would provide a seamless

in-store experience which would lead to enhanced capabilities of the company’s

field teams. Vendors who regularly employ data in their decision making are

more susceptible to enable their field sales team, which significantly would

improve their effectiveness and overall performance compared to those vendors

who fail to adopt such a data-friendly approach towards their organizational

culture.

Measuring Field Sales team Performance and ROI: Field managers who are able

to measure their team’s ability to drive sales at the store level using POS data,

they can prove their own value. This way they could emphasis on the overall cost-

effective and delivery of strong ROI. For this field managers need to confirm a

net-positive return through indicators such as sales lift, in-stock percentage,

brand awareness, and store associate knowledge.

Following are the ways in which brands are utilizing POS data.

Improvement of forecasting: By using POS data, companies can predict their

future sales, performance and also avoid pre-empt storing of sticks to respond to

fluctuations in demand.

Insights into new product launches: POS data allows vendors to gain full visibility

on their launches almost immediately. By analyzing performance against

benchmarks that were set during the planning process, brands can easily

recognize areas of success or failure, proactively making changes to maximize

profitability in the early stages of a new product launch. As competition increases

in the market, companies release new products to stay relevant in the market,

showcase their brand’s transformation and growth. Therefore, measurement of

POS of new product launches is important.

Enhancement of promotions: Be it a new product launch, or updated old

products, promotions are important since they improve ROI and showcase the

product effectively to the market. Through POS data, brands now have a better

clarity on the effectiveness of their promotions through tracking of inventory at a

more granular level. This also helps brands to course-correct early on to ensure

that their promotions don’t flop. In the end, POS data help brands to showcase a

strong ROI for their past promotions to convince the buying teams to reinvest in

future promotions.

Reduces out of stocks: Currently, out-of-stocks is a problem faced by almost every

brand selling into retail. This could be curbed by careful monitoring of products

through in-store inventory data as well as the available POS data. This way sales

team can rectify issues before they become serious, significantly affecting future

sales due to out-of-stocks.

Though many companies have started to implement POS data into their systems, the

tools and processes they are using are outdated which hinders their ability to uncover

value-added insights that could lead to a better growth of the company.

Various research papers suggest the application of multiple linear regression models to

convert POS data to the predictors that can significantly improve the demand forecast of

the companies. Surveys indicate that most of the companies are looking for POS

solutions that provide sales planning and forecasts, dashboards and retail scorecards,

automated collection of data, self-service access for business users, a mobile application

for teams, integration of syndicated and market-level data, ship-to-consumption

analytics, push notifications for key metrics etc.

Though companies have been using Excel as a generic solution to analyze their POS

data, it has long been losing the market. It is time for brands to shift to generic business

intelligence solutions such as Tableau, SAP or retail-specific solution such as Askuity or

RSi which are specifically designed to help retail brands analyze common retail metrics

such as Sales Dollars/Units, Margin Dollars/Units, Inventory Turns, Average Price,

GMROI, etc. Such purpose-built solutions have an advantage over conventional

spreadsheets by removing significant barriers in data management, automating the

downloading of data from retail reports while simultaneously harmonizing product data

across every retailer.

As the retail environment in evolving and the competition is day-by-day increasing,

brands need to stay relevant, updated and efficient in order to stay above their

competitors.

Leveraging POS data and applying their insights into their supply chain

needs to be a priority.