- On October 8, 2018
Welcome back to the third post in our four-part blog series on pricing strategies for C-Store operators. In the previous installment we addressed how C-Stores can promote smarter and more profitably with effective forecasting, modeling and design. Find the post here
Declining trip frequency has put a chill into the hearts of convenience store operators. At its State of the Industry Summit last winter, NACS disclosed research that showed the average number of weekly store visits had fallen an average of one trip per customer per week, from 3.6 in 2014 to 2.6 in 2017, for a three-year decline of 27.7%.
“Even if we increase the market basket spend, we cannot make up the entire spend from this trip loss,” worried Andy Jones, president and CEO of Sprint Food Stores and a member of the NACS Research Committee.
C-Stores battle stiff competition from fast-food channels, which have used aggressive deals on beverages like coffee and fountain drinks to siphon away market share since 2016. Chain drug store expansion has put packaged beverages and snacks and (in some states) beer on more street corners. Digitally-enabled food delivery services, like GrubHub, DoorDash and Uber Eats and have re-defined the meaning of convenience for some customers.
Yet these pressures on C-Store trips also come at a time of otherwise strong industry performance:
- In 2017, all of the top 10 merchandise categories, inclusive of tobacco, saw positive dollar sales growth, driving total forecourt sales to a record $237.0 billion through 155,000 locations, according to data published by NACS.
- C-Store industry gross profits increased 6.1% year-to-year in 2017, NACS reports.
- Gross sales in the convenience/gas sector increased 14.8% in the first 7 months of 2018, according to IHL Group.
With an average transaction count of 1,100 shoppers per store per day (NACS), every visit presents an opportunity to deliver shopper satisfaction, learn something about shopper response, obtain customer insights, and earn a fair margin.
What’s the price of loyalty?
To make the most of this opportunity, convenience store operators are investing in card-based, mobile apps and card-less loyalty programs – including popular refill mug programs – with the aim of encouraging repeat visits, increasing transaction sizes, attracting trade funds, and capturing detailed, actionable sales analytics. Their aim is to build the kind of targeted long-term relationships with customers that drive more frequent, more profitable sales.
To earn loyalty from shoppers, C-Stores must show loyalty to shoppers with a compelling every-day value story and interesting offers. Trusted and consistent pricing is one of the most effective ways to get the message across.
To support their loyalty efforts, C-Store operators manage pricing with several objectives in mind:
- Price Image. Use price strategically to gain a reputation as a place for great value every day.
- Shopper Retention. Build everyday pricing trust to earn repeat patronage over the long haul.
- More Visits. Offer attractive specials and everyday deals to give shoppers more reasons to stop in.
- Larger Transactions. Identify which value prices will encourage shoppers to take home larger baskets.
Loyalty programs open a value-added communications channel with customers. Consistently dependable prices are a core element of your trust message to shoppers. They define whether your stores are perceived as a valued resource or just the nearest place to stop at a given moment.
Machine learning delivers loyalty insights
A well-conceived loyalty program will also yield a torrent of data about your customers that is relevant to your pricing strategy and personalization efforts. Extracting business value from these insights requires analytic tools that can simultaneously and continuously track multiple dimensions:
- Behavioral – Measure how often they visit, what time(s) of day, and how they respond to promotional programs.
- Market basket – Capture and analyze whole-transaction POS data to what items they buy and in what combinations.
- Lifetime customer value – Track current spending levels and forecast how much they are likely to spend with you over time.
Machine learning can be used to segment shoppers along relevant dimensions and deliver personalized offers to higher-potential customers that can drive more frequent shopper visits and larger basket sizes.
“Instead of giving discounts to every customer, we started discounting more exclusively for our loyalty customers,” wrote Dan Durbin, former president of R.L. Jordan Oil Co., in a 2018 article in CSPdailynews.com. “That’s one of the smartest things we ever did for our brand.”
Want to learn more? Download our free Industry Guide, “Power-Up Your C-Store: Four Dynamic Actions for Customer-Driven Pricing, Promotions and Loyalty” https://cleardemand.com/cstoreguide/
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Next week in this series: Find margin opportunities and implement price optimization on relevant items.