Tag: solutions

20 Dec 2016

Six Reasons Why Retailers Need Dynamic Competitive Intelligence Solutions: Part II

Traditional Comp Shop Programs Don’t Account for Omni-Channel

Omni-Channel is no longer just a buzz word. Millennials now represent over 50% of retail spending. Both they and your traditional shoppers expect retailers to meet them wherever they like to go and however they prefer to interact. As a result, retailers are increasingly testing and rolling out click-and-collect and delivery programs. According to IBISWorld, online grocery sales are expected to increase 9.5% annually to become a $9.4 billion industry in 2017.

With all this in mind, retailers are beginning to realize that they can’t just look at the in-store data anymore—in fact now it’s much more cost-effective to use online data and augment with directed in-store checks. It becomes even more critical that retailers leverage solutions that help them understand where they can responsibly leverage online data (because sometimes the assortments don’t fully match) and where to use in-store data. Retailers’ online vs. in-store strategies are constantly changing, and so a hybrid approach becomes even more critical to maintain complete visibility.

Inflexible Programs

Woman-StretchingEngage3 market data demonstrates that while many categories and departments are still being priced at a national level, there is a strong trend towards increasingly localized pricing. It’s these location-specific products and categories that can be detrimental to a retailer seeking true visibility, because KVI lists and comp shop budget dollars are not allocated and reallocated appropriately. Far too often we see that many retailers’ competitive intelligence programs still leverage static, banner-level lists by which margin opportunities are missed and price reputation is threatened. It’s imperative that competitive intelligence programs be able to respond to market changes and leverage real-time analytics to maximize ROI.

Tactical, Rather Than Strategic Focus

Most retailers today put too much emphasis on the competition’s prices as they change, rather than the strategies that are being employed at the root of those changes. The comp shop processes of old produced data that was far too old and stale by the time it was received, and thus it has historically been much more difficult to discern the competition’s pricing and assortment strategies. Retailers need the ability to take a step back, see the bigger picture, and truly understand the competitive landscape and strategies that are being deployed around them.

Wrong Measurements

Confused Measuring Panda

We’ve seen that most pricing departments are responsible for managing a budget, however, oftentimes they aren’t responsible for measuring the quality or exact value that the program returns. How does your team measure the effectiveness or value of your competitive intelligence program today? We’ve seen over time the incredible value of appropriate Key Performance Indicators (KPI’s), or measurements, being defined and maintained to evaluate the program. KPI’s may include find rates, accuracy, competitive price visibility, completion rates, Return on Investment, and more.

05 Dec 2016
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Six Reasons Why Retailers Need Dynamic Competitive Intelligence Solutions: Part I

As the in-store and digital customer experiences rapidly converge, retail sales are being attacked from all directions. Powerhouses like Amazon, Walmart, and other online retailers have developed aggressive pricing algorithms with the power to change a price multiple times in a single day. Sophisticated players are even changing the prices on 10-20% of their online assortment daily (and prices on their top items as frequently as 3-4x per day). Other threats to retailers’ price image include the continued growth of hard-discounters and dollar stores and the proliferation of private label products.

Hi All, my name is Kevin Johnson and I work in Product Marketing for Engage3. I’ve spent the better part of four years in the competitive intelligence industry and I’ve spoken with hundreds of different retailers in the space about pain points and inefficiencies as they persist today in so many organizations. This article is meant to be a two-part culmination of what I’ve learned about the evolution of retail technology and comp shops. I’ll spend time reviewing some of the key challenges facing those operating in the “Old World” of competitive intelligence and why it is absolutely critical to have a dynamic solution to combat these challenges. Please don’t hesitate to reach out here if you think there’s something I may have missed – my “door” is always open.

Typical regional comp shop programs today leak up to 65 basis points* of margin largely due to inaccurate data and pronounced gaps in ability to respond to these dynamic price changes quickly. However, new technologies and data-driven processes are emerging to help traditional retailers adjust in this new world of price transparency, hyper-competitiveness, and shifting consumer behaviors. At Engage3, we see more often than not that retailers don’t always have the visibility they need to make informed pricing decisions such that both profit is maximized and price image is maintained. It’s become apparent that traditional, static approaches are not suitable anymore. The problems that we’re constantly seeing are as follows:

Poor Visibility, High Error Rates, and Low Find Rates

Due to the combination of slow competitive product and price check cycles, expensive and error-prone data collection processes, product linking complexities, departmental resource constraints, and the ever-shifting competitive landscape, it is increasingly difficult to achieve the right level of competitive visibility. The “Old World” of competitive intelligence does not allow for intelligent ways to match products across retail chains, which results in low find rates and leaves the door wide open for natural human error.

Retailers working within this “Old World” of competitive intelligence are exceedingly at the mercy of the labor collecting the data. When we begin working with a new client, we on average see error rates between 20%-40%, find rates between 50-80% and low overall visibility. Whether you use a price optimization system for execution or have a more manual approach to pricing, these challenges contribute significantly to the aforementioned 65 basis point leakage.

Comparing Apples to Oranges

Apple-not-equal-to-orange

Retailers are flying blind in an age where private label products are becoming increasingly commonplace. According to Nielsen, private label products share of retail sales grew from 16.2% to 17.4% between 2009 and 2011, with year-over-year sales growth outpacing national brands by a significant margin. The trend highlights the importance of quality product linking methods as store brands continue to consume market share. Without accuracy in like-for-like products, retailers are getting little or no value for the money they spend on competitive intelligence. How can they, when their auditors are unknowingly collecting data for apples instead of oranges?

 

More to follow…