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Author Archives: MHarper

Join Us at NRF For a Big Ideas Session: Smart Data, Not Just Big Data

January 14th, 2016 | Posted by MHarper in Retail - (Comments Off on Join Us at NRF For a Big Ideas Session: Smart Data, Not Just Big Data)

Join APT this Monday, January 18th for a Big Ideas session about “Smart Data, Not Just Big Data”. We’ll be discussing innovations that retailers are considering today and why it’s important to use a Test & Learn approach to understand whether each of these ideas work. Executives from APT clients will present at the session.

Click here for more information.

How big data is changing retail and restaurant businesses

October 28th, 2015 | Posted by MHarper in Restaurants - (Comments Off on How big data is changing retail and restaurant businesses)

APT is featured in an Information Age article on how retailers and restaurants can use big data to gain an accurate view of performance and try new ideas to improve profitability. Click here to read the full article, which includes insights from the APT Index.

How big data is changing retail and restaurant businesses

October 28th, 2015 | Posted by MHarper in Retail | Uncategorized - (Comments Off on How big data is changing retail and restaurant businesses)

APT is featured in an Information Age article on how retailers and restaurants can use big data to gain an accurate view of performance and try new ideas to improve profitability. Click here to read the full article, which includes insights from the APT Index.

Making Big Data Make Money in Retail

October 8th, 2015 | Posted by MHarper in Retail | Uncategorized - (Comments Off on Making Big Data Make Money in Retail)

The importance of challenging conventional wisdom and getting beyond the hype of scale

By Jim Manzi

Any experienced businessperson has seen this movie before with earlier technologies ranging from the World Wide Web to CRM to enterprise data warehouses. It’s the plot in which a technology goes from promise to hype to true application. Big data is now deep into the hype phase of this cycle. All the classic signs are there: You can eat buffet dinners all 52 weeks a year at big data conferences. Big data tag lines are now common in emails from industry analysts, and even investment bankers are tossing around the phrase. But as with these other innovations, there is real substance at the root of the hype. And – like CRM, the web, and data warehouses – big data is a big part of running any large corporation in the future.

Profitably exploiting the emerging opportunity for big data will require using some of the key learnings from companies that have already gone beyond the hype: first, an unwillingness to be snowed by conventional wisdom and technical jargon; second, the ability to act quickly at low cost, learn what works from trial-and-error experience, and then reinforce strengths; and third, a ruthless focus on profits as the success criteria for proposed investments of time or money. Those three characteristics will be necessary as data moves beyond conventional storage capacity and into the cloud. And those characteristics will be critical as retail executives balance the immensity of scale with the practicality of business applications. In short: Big data consists of small data. The challenge is to take the right data and make it drive decisions.

In the end, the transaction data remains the most important. It will show the retailer what makes consumers trade their money for our goods and services. Smart consumer businesses ignore these external data feeds or rebuild their infrastructure around them. Instead they use abstractions to extract most of the analytical value, while only needing a tiny fraction of the data volume.

The thing that is clear is that today – right now – large consumer companies can begin taking advantage of many of these data streams by capturing them at an abstracted level, incorporating them in data schemas, and using them to improve decisions.

The Humility of Test & Learn

Retailers and other executives are aware that data needs to drive decisions rather than drive them crazy. Through careful experimentation to test new programs and approaches, the most serious industry analysts have started to recognize that Test & Learn is central to making big data create value.

A Test & Learn capability for a major marketer requires a specialized analytical platform, but also has several process and organizational components. The starting point is executive commitment. The person or small group with ultimate operational responsibility for shareholder value creation, typically the CEO or president, must legitimately desire reliable analytical knowledge of the business. Second, a distinct organizational entity, normally quite small, must be created to design experiments and then provide their canonical interpretation. Third, a repeatable process must be put in place to institutionalize experimentation as a part of how the business makes decisions.

Here are two examples. First, let’s look at Wawa, a 645-location convenience store chain. Its marketing team had developed a new flatbread breakfast offering that had performed well in spot-testing. Management wanted more robust measurement of what happened to other products when the flatbread was introduced. Wawa used APT’s Test & Learn software to design a scientific test and measure the impact of the flatbread introduction across all key performance metrics and product categories. The flatbread performed well. Unfortunately, the new item was so enticing that it cannibalized sales of existing menu items. Wawa decided not to roll out the flatbread.

Number two: At Subway the debate surrounded whether they should launch a low-cost $5 product. A set of Subway franchisees had implemented a promotion selling their famous footlong sub at $5. While some franchisees were convinced that the promotion was driving incremental sales, others were skeptical. APT’s Test & Learn software compared the performance of franchisees that had implemented the $5 Footlong versus a scientifically matched group of restaurants that had not implemented the promotion. The software showed that the $5 Footlong was profitable, and executives decided to launch the $5 Footlong nationally.

These two examples show companies that have access to a staggering amount of information. They also show two companies that incorporated a systematic method of feeding that data with practical experiments that expand (or manage) key operational and marketing decisions. The systematic focus produced the right data. It cut through the clutter and produced clear results.

Test & Learn, at its heart, is a simple concept for a business promise that is begging for simplicity. The orientation should not be toward big, one-time “moon shot” tests, but instead toward many fast, cheap tests in rapid succession whenever this is feasible. The goal is to build a mountain of pebbles.

The Future of Customer Data for Restaurants

August 5th, 2015 | Posted by MHarper in Restaurants - (Comments Off on The Future of Customer Data for Restaurants)

APT SVP Jonathan Marek discusses the growing importance of collecting and generating insights from customer data. Watch this video to hear his thoughts.

APT Index: May In-store Retail Sales Increased [+0.4%], with a Strong Mother’s Day Weekend [+1.0%]

June 11th, 2015 | Posted by MHarper in Retail | Uncategorized - (Comments Off on APT Index: May In-store Retail Sales Increased [+0.4%], with a Strong Mother’s Day Weekend [+1.0%])

APT announced today that the APT Index of in-store retail sales increased [+0.4%] nationwide in May 2015, compared to May of last year. APT Index retail sales were up [+1.0%] on Mother’s Day Weekend and down [-0.7%] on Memorial Day Weekend. Click here to read the full report.

APT Index: May Restaurant Sales Increase [+3.3%], with Strong Growth on Memorial Day

June 11th, 2015 | Posted by MHarper in APT Index - (Comments Off on APT Index: May Restaurant Sales Increase [+3.3%], with Strong Growth on Memorial Day)

APT announced today that the APT Index of restaurant sales increased [+3.3%] nationwide in May 2015, compared to May of last year, with an even higher increase on Memorial Day Weekend [+5.2%]. The APT Index of quick-service restaurant (QSR) sales increased [+5.2%], while the APT Index of full-service restaurant (FSR) sales increased [+1.4%]. Click here to read the full report.

APT Index: April In-store Retail Sales Down [-1.0%], In-store Apparel Sales Down [-3.6%]

May 12th, 2015 | Posted by MHarper in Retail | Uncategorized - (Comments Off on APT Index: April In-store Retail Sales Down [-1.0%], In-store Apparel Sales Down [-3.6%])

APT announced today that the APT Index of in-store retail sales for April 2015 decreased [-1.0%] nationwide, compared to April of last year. The APT Index of in-store apparel sales was down [-3.6%], though APT reported online apparel sales up [+2.8%] in April, as digital channels become a larger portion of total sales. Click here to read the full report.

APT Index: April Restaurant Sales Up [+3.6%], Marking Another Month of Strong Growth

May 12th, 2015 | Posted by MHarper in APT Index - (Comments Off on APT Index: April Restaurant Sales Up [+3.6%], Marking Another Month of Strong Growth)

APT announced today that the APT Index of restaurant sales for April 2015 increased [+3.6%], compared to April of last year. The increase was driven by an increase in check size [+3.4%]. The APT Index of quick-service restaurant (QSR) sales increased [+5.9%], while the APT Index of full-service restaurant (FSR) sales increased [+2.3%]. Click here to read the full report.

APT Index: March Same-Store Restaurant Sales Increase [+3.5%]

April 13th, 2015 | Posted by MHarper in Restaurants - (Comments Off on APT Index: March Same-Store Restaurant Sales Increase [+3.5%])

APT today announced that the APT Index of in-store restaurant sales for March 2015 increased [+3.5%] compared with March of last year, as Americans’ view of the economy reached an 8-year high. APT Index quick-service restaurant sales increased [+6.2%], and APT Index full-service restaurant sales increased [+1.6%]. Click here to read the full report.