printer friendly version

Visual business intelligence

Visual business intelligence (VBI) has been used in the retail market for buyers' behaviour analysis and it can not only analyse customers' behaviour for security purposes, but also help retail owners to operate their businesses more efficiently.

The days of storeowners making the best decisions for their business based on personal experience with customers have long past, due in part to a competitive global market and the complexities of customer relationships in the Internet age. Retailers have invested heavily in data collection and last year spent US$23 billion on the application of that data according to better understand customer behaviour and run their businesses more effectively, according to AMR Research.

With 75% of retail buying decisions made in stores at the time of purchase according to the analysis of National Retail Federation and a new focus on the growing power of the customer, understanding individual physical customer behaviour has become more critical for retailers.

Analysing online customer behaviour addresses less than 1% of all retail sales and until today, the methods for tracking physical customer behaviour have been expensive, complex, inconsistent and not scalable.

This article introduces visual business intelligence (VBI) and an innovative new solution to better understand individual customer behaviour, which proposes to generate millions of dollars in revenue and savings for retailers worldwide.

The missing piece in retail business intelligence

In order to make better business decisions, retailers continue to invest extensively in the collection of transactional and operational data. The volume of this data is significant. For example, a 100-store specialty retailer will have upwards of 100 000 stock-keeping units (SKUs) and process nearly 30 000 transactions a day. Most department stores have a million SKUs or more. With the continued growth of e-commerce, retailers have also invested significantly in online data collection.

Data is just one step in a process and in 2006 retailers spent $23 billion on business intelligence (BI) in order to apply all that data to business decisions.

The complexities of a global and competitive marketplace with access to online resources has changed retail from a top down 'we pick the products and set the pricing' to a bottom up 'the customer is in charge' strategy.

This 'de-massing' of the mass market has retailers rushing to build a better understanding of the individual customer. The National Retail Federation supports this new focus in a recent report which stated that 75% of all in-store retail purchase decisions are made in the store at the time of purchase. The missing piece becomes quite clear. Business intelligence is lacking the analysis of physical customer behaviour, which is the function of VBI.

Visual business intelligence - more than meets the eye

Visual business intelligence is knowledge based on the application of visual data to a business problem or opportunity. Although it appears to be a simple proposition, there is more to VBI than meets the eye.

The process of VBI starts with a collection of visual data which is analysed to create unique information. This information becomes knowledge when it is applied to a business problem or opportunity. For example, visual data is collected from a retail store (the number of customers who enter an area of interest, the time they spend there and what direction they travelled). This data is analysed and becomes information (the answer to a simple question such as how long are people waiting in line?).

In turn it becomes knowledge (the application of the data to business such as reducing wait times and decreasing departures by opening additional cash registers between 11 a.m. and 2 p.m. on weekends or when more than five people are waiting in line). The opportunity then exists to move further to forecast and even model physical customer behaviour.

Tools of the trade

Existing technologies that track and analyse human behaviour have not met the needs of the market due to one or more critical limitations, including complexity of implementation and integration, high cost, inconsistency, and lack of scalability.

New VBI applications can be integrated into existing IT infrastructure, offers scalability and portability. It enables retailers to make better tactical and strategic business decisions by integrating individual physical customer behaviour knowledge, which can generate millions of dollars in revenue and savings.

Benefits of VBI for retailers

Four areas of retail business management that can benefit from VBI include marketing and customer relations, merchandising, operational efficiency and performance management. The following are example applications of VBI in each area:

Increased individual product sales

Apply product 'attraction' data (the number of people who look at a product and how much time they spend looking at it) to sales revenue data to create unique knowledge on product sales.

Improved product presentation

With physical customer data, retailers can have a better understanding of what attracts customers and how they respond to an environment, product presentation, and physical product positioning.

Improved marketing ROI

Applying in store physical customer behaviour data to marketing data and sales results provides a more robust understanding of marketing ROI.

Higher quality point of purchase (POP) marketing

With data such as how much time customers spend at the POP display and what customers are attracted to compared to what they buy, better decisions about POP can be implemented.

More shopper satisfaction

Understanding customer behaviour patterns, such as how many people enter a store, where they travel and how much time they spend, can offer insight into how customers view their experience.

Improved product packaging and presentation

With physical customer data, such as the number of people who look at a product and how much time they spend looking at it, retailers can have a better understanding of what attracts customers and how they respond to packaging.

Increased profit margins

Comparing sales results to the analysis of customer behaviour in relation to a product or category can offer insight into better margin opportunities.

Reduced register wait times

Understanding register line wait times positively affects register staffing management. Realtime data analysis can provide the opportunity to react to needs on-demand.

More efficient floor space utilisation

With physical customer data such as aisle traffic, retailers can have a better understanding of how customers respond to an environment.

Reduced departures

Analysing data of store traffic against sales to determine departures, and further application of data such as register wait times, can offer insight into departures.

More efficient register management

Customer wait time at the register compared to time of day data can provide knowledge for register management and the opportunity to react to increased customer traffic in realtime.

Better counter staffing

Employee counts and length of time at a sales counter can be tracked and managed according to sales and customer traffic to improve sales and customer experience.

Improved staffing policies

Data such as store entry and exit traffic and specific category or department traffic compared to day of the week and time of day data, as well as sales results, can provide the understanding for more efficient staffing management.

Share this article:

Further reading: