Page 1 ... Prior to the 1970's, retailers used very few methods in setting product prices, ... Price optimization techni
Competitive Shopping The Past, Present and Future Competitive shopping is a process that most retailers perform in order to make short-term decisions based on local competitive offerings. It is, however, just one component of what is known as Competitive Intelligence. What is Competitive Intelligence? Competitive Intelligence is the action of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors, and any aspect of the environment needed to support executive strategic decisions. Competitive Intelligence consists of Strategic Intelligence, a long-term view of factors affecting an organization’s competitiveness, and Tactical Intelligence which is focused on providing information designed to improve short-term decisions. In retail, Tactical Intelligence often utilizes a competitive shopping process as a cornerstone data gathering tool. It is most often used with the intent of growing market share, revenues and profitability. It investigates various aspects of the Marketing Mix, such as:
Product – what are people selling?
Price – what price are they charging?
Promotion – what activities are they conducting for promoting this product?
Place – where are they selling this product?
Historically, the speed at which Tactical Intelligence is gathered has been largely unchanged. Ironically, the number and speed at which competitors change prices has increased immensely. According to Forbes Onlinei, Amazon changed prices on as many as 80 million products in one day, during last year’s Christmas season. How retailers went from a handful of price changes back in the 1980’s to millions is hard to fathom. Understanding how we have evolved in our need for versus how we collect Tactical Intelligence will help us understand why collection processes have failed us. The Past Prior to the 1970’s, retailers used very few methods in setting product prices, relying mainly on rulesbased techniques. Most prices were set on a margin technique with a handful of image items being indexed to the competition. Thereafter, prices only changed when the underlying cost changed, or when the competitor changed their price. In non-inflationary times, price changes were not performed in great numbers. Starting in 1970, through 1990, retail consolidation became a factor as bigger box retailers hit
the scene with value, club and category killer formats. Retailer’s dependence on competitive shopping intelligence increased as competition grew fierce and smaller retailers were driven out of business. The Present The introduction of e-commerce in the 1990’s has taken retail by storm, thus disrupting the way shoppers fundamentally look at and seek their definition of value. Retail giants have emerged, creating supply chains that are highly efficient and cost savvy. Retailer specialization has narrowed demographics and markets. Shoppers have more choices of where they shop, how they shop, and when they shop. Since the economic downturn of 2007-2008, consumers have redefined their definition of value, creating even more pressure for retailers to be competitively priced. Competitive Shopping Techniques Since the early 2000’s, computing power has allowed businesses to use big data to determine many things that are important to customers. Price optimization techniques have flourished and are a part of any smart retailer’s toolbox. These methods help the retailer determine how to price their portfolio in order to drive profitable demand and customer satisfaction. In addition, shelf management techniques have followed suit. The retailer can use data driven processes to determine the assortments that should be carried and the space allocated to each. Unfortunately, the process for competitive shopping has remained relatively unchanged. For the better part of the millennium, retailers have used similar techniques that were available in the 1980’s. These practices have been slightly automated, but have not kept pace with the ever changing competitive landscape and the ability to make real time price changes. Keeping up with the competition, due to the retailers’ inability to react in a timely fashion, is not only frustrating, but painful. Unless the retailers evolve, their lack of agility will ultimately damage our brand. What are the Barriers to a Successful Competitive Shopping Experience? The barriers in the process today stem from:
Inefficient Planning: The retailer must choose what and where to competitively shop. This can take some time depending on how the task is performed… ie: whom we shop, whether we are shopping key items or a full category, department or store. Many times retailers use their previous experience for this task when scientific approaches are much more efficient. A “gut” approach often leads to stale item lists from the same store locations without considerations in marketplace changes. Many retailers use a “one-size fits all” approach. They use the same list across all pricing zones rather than a focused approach which recognizes differing customer preferences.
Ineffective Execution: Once retailers know what and whom to shop, they must execute the shop. Whether internally, or by employing an outside service to complete the task, this process is regrettably subject to a great deal of error. With the proliferation of brands and sizes unique to particular competitors, errors in this process can be as much as ten to forty percent. With such a high potential error rate, quality control reviews have to be undertaken causing us to either scrap the erroneous data or re-shop the items in question, causing further delays in our competitive response.
All together, these steps can take days, weeks or longer. By the time the retailer has the data needed to react, they are late to the dance. All the good dancers have partners and the retailer is left with the partner (customer) with two left feet. The Future (Is Now) Big data has allowed retailers to plug into the shopping experience, determining what the customer is telling us through their shopping patterns, in real time so decisions can be made now. Is there a process that provides this same speed and accuracy into competitive insights? The simple answer is yes! And a dynamic problem requires a dynamic solution!
I was at a conference this month, reviewing an offering that I think is going to disrupt the way retailers plan, gather and analyze competitive shopping data.
Engage3 Vantage Engage3 Mission Control This suite tackles all of the shortfalls that I have experienced in trying to obtain competitive data. It addresses the need for speed, accuracy, consistency, frequency, and cost. Their Comp Shop Planning module, named Vantage, recommends the most effective comp shop program. It provides data-driven recommendations for markets, locations, competitors, items, frequencies and other parameters, designed to generate a statistically valid sample while minimizing cost and maximizing competitor price visibility. It provides deep insight into competitors’ location, category, and item strategies. Mission Control is the competitive price shop management platform. From this dashboard, retailers can schedule and monitor in-house or 3rd party field auditors. It includes built-in features to increase the speed and accuracy, including real-time error correction features, which alerts auditors of potential data entry mistakes while they are still in the store. And the data is available and quality checked to a 97% accuracy level, immediately after the shop rather than days or weeks later. These features alone make my process better, but then it goes several steps further. Much like new big data techniques that can predict consumers’ response to changes in price, similar techniques are used to determine a competitive response in the marketplace. Using predictive analysis techniques, more accurately referred to as “Predictive Pricing Signals”, it can not only predict when, but if an item will have
a price change. This gives retailers a significant advantage in determining the appropriate items to shop based on their likelihood of a price change. No more spending money on items that don’t tend to change unless the signals indicate a price change is a real potential. For instance, if predictive analysis indicates a price will remain unchanged, I can reallocate the cost to an item that has a high probability of change, providing better overall coverage and more useful competitive information. How many times have you price checked the same items, only to keep the same strategy since the competitor did not change price? This reallocation of budgeted competitive shopping dollars buys a deeper visibility into your competitor’s pricing, and thus strategy. This methodology provides a reported 3X increase in the ROI of traditional methods. That results not only in my competitive shopping dollars providing a better return, but gives me quick turnaround and insight, allowing me time for a timely response to those strategies. This is a real game changer in the competitive shopping process by providing accuracy, effectiveness of spend, timeliness and insight into competitive strategies that have not been previously available. For those that have been as frustrated with the process as I, a new day is here! Hats off to the problem solvers at Engage3. i
Forbes On-line, “Amazon’s Pricing Strategy Makes Life Miserable for the Competition”, November 20, 2014, Walter Loeb, Contributor.
Article written by Mark Kelso, Founder and Managing Partner at Price Revolution, LLC. Mark Kelso is a price optimization expert, who, in 2011, created a unique consumer focused strategy development process that has resulted in substantial improvements in retailer’s key performance measures. Retailers using this targeted technique have enjoyed dramatic increases in revenue, profitability and consumer demand for their products. Visit Price Revolution at www.price-revolution.com
