Modernizing Retail: Closing the Consumer Data Gap

Bridge Over Gap

Written by: Danny Bogus, Principal Digital Consultant

Today’s world is dominated by organizations that collect and utilize an ocean of consumer data.  Companies like Netflix, Amazon, and Google have grown with unprecedented speed and scale by leveraging a data-driven strategy.  Even supermarkets and gas stations have evolved away from once-anonymous transactions, and into loyalty card transactions that drive sophisticated promotions and retention programs.

The first step in a data-driven strategy is always the same regardless of the company; capture consumer data with accuracy and abundance.  With the big data revolution underway, lotteries must implement data-capture initiatives in order to remain competitive and relevant in the new age of commerce.

Lottery transactions on the retail system remain anonymous in nearly all jurisdictions today.  As a result, insufficient information is available to determine basic insights such as an average shopping cart value.  Even a small data point like this can become a huge driver of future innovation.

For example, player shopping cart values offer vital information in determining the economics of key retail modernization initiatives such as cashless payment acceptance.  Cashless processing costs carry both fixed and variable costs, and small shopping cart values can result in exorbitant fees to lotteries and retailers.

Additionally, part of the business rationale to justify cashless acceptance is that existing consumers will increase spending.  But without a pre-existing basis being measured, there is little way to prove that an existing player switching to cashless results in more revenue.

As another example, advertising efficiency can improve with the direct measurement of player activities at retail.  Over time, purchase patterns will formulate for players and any deviations (intentionally driven by advertising) can be better observed.  Player-identified transactional data can go a step further by identifying if advertising campaigns resulted in new players or increased spend from existing players.

Many lotteries have installed second chance and loyalty programs that can collect some individual purchase data downstream.   This represents an important step forward in understanding players better.  However, ticket-entry data has drawbacks that must be considered when drawing conclusions from analysis activities.

For example, if a loyal customer doesn’t have time to manually submit tickets each day or week, then the data will falsely indicate that the player has less activity and value.  The collection of tickets over time before submission also distorts insights related to actual purchase frequency at retail.  Moreover, with a ticket entry program, there is always a risk that those entering tickets are not always the original purchasers.

So how can the industry embrace a data-driven strategy, given some of the current challenges and gaps in data collection?  First, lotteries must examine their current programs, systems, and opportunities in order to identify quick wins for expanded data capture.

As one possibility, some states currently require players to scan identification for age verification on self-service machines prior to the purchase of tickets.  It may be possible to simply log a unique player data element into the transaction logs.  Over time, this player-tagged data could generate a picture of player shopping cart values, frequency of visits, and monetary spend.

As an additional possibility, some states offer a ticket checking system on their website or mobile app.  Players can scan a ticket to find out if it is a winner.  If this feature was placed behind a player registration and login, then the scan data could be compiled per player.  Over time, this data can be analyzed to determine purchase patterns and game preferences.

A few states have experimented with a player loyalty card that activates at retail terminals.  Programs of a similar nature have found success in Europe and Australia in the past, however state lotteries have not yet discovered a model that results in widespread adoption.  This approach can lead to tremendous insights for the industry but requires long-term planning, execution, and resourcing.

It should be recognized that most consumer-packaged good companies do not have the ability to track consumer data within retail.  This is because the retailer’s system is handling the transactions and logging any player-identifiable elements via shopper’s card and loyalty programs.  Perhaps it is possible for lotteries to structure a partnership with retailers in order to share data in cases where lottery is being logged through the retailer’s point-of-sale equipment.

As seen in other industries, the ability to leverage data in order to drive growth and innovation is proven.  However, lotteries must first close the gap that exists in data collection today.  There are many opportunities available but more experimentation is required to discover the best way forward.