How
retailers can improve price perception—profitably
New
methodologies, powered by big data and advanced analytics, can help retailers
attract value-conscious consumers without sacrificing margins.
As retail executives know all too well, most pricing decisions require a
trade-off between margin and price perception. To avoid a “race to the
bottom”—the self-defeating exercise of trying to beat every competitor’s price
on every item—retailers must hone their ability to make smart pricing investments.
Indeed, the savviest retailers identify key value categories (KVCs) and key
value items (KVIs)—those product categories and SKUs whose prices consumers
tend to notice and remember. If a retailer can do this accurately, it can price
those specific products competitively while charging higher prices on other
items.
Yet, despite the importance of KVC and KVI
identification, many retailers still lack a systematic, fact-based process for
doing it. Some retailers rely almost entirely on the commercial intuition of
experienced category managers. To be fair, a number of retailers do use data to
try to isolate KVCs and KVIs: for example, they might benchmark their
assortment and prices against those of discounters, on the assumption that
price-sensitive consumers use discounters as a baseline for comparison
shopping. Some retailers apply a simple heuristic—they use a combination of
weighted criteria such as purchase frequency and brand perception to select
KVIs.
But in today’s data-rich business
environment, retailers can—and certainly should—go beyond these basic
techniques. To accurately identify KVCs and KVIs, leading retailers tap into
the treasure trove of transaction data, loyalty-card data, and online research
available to them. They use sophisticated methodologies that require the
ability to analyze billions of transactions and hundreds of gigabytes of data.
Harnessing the power of advanced analytics to improve price perception can have
significant impact: a margin boost of one to two percentage points, with steady
or even increasing sales volume.
Which
categories and items affect consumer price perception?
Broadly speaking, products can be
classified into two groups: frequently bought items (purchased twice a month or
more often) and infrequently bought items. Most grocery items fall into the
former classification, but grocery retailers—particularly hypermarkets, which
have higher shares of nonfood products—also carry infrequently bought items. By
contrast, the assortment of home-improvement retailers consists mainly of
infrequently bought items such as power tools and home appliances. Our
recommended methodology for identifying KVIs and KVCs differs slightly for each
of these two product groups.
Ideally, KVIs will account for 15 to 25
percent of sales in the category. Other products in the assortment are
classified as either “foreground” or “background” items
Identifying
KVCs and KVIs among frequently purchased products
For frequently bought items, retailers can
select KVCs by calculating a normalized score for each category based on three
criteria: frequency of purchase (weighted at 40 percent), customer reach (40
percent), and promotional share (20 percent). Then, to identify KVIs, retailers
can take four sequential steps, each of which involves the use of big data and
advanced analytical models and calculations.
·
First, identify SKUs that are a ‘good deal’ or
represent good value for money. These SKUs are either
cheap relative to the category or have a low per-unit price. A two-liter bottle
of soda, for example, might qualify as a good deal whereas a half-liter bottle
might not, since the two-liter bottle’s price per liter is much lower. These
calculations should be done for every item for every week of data, to correct
for any temporary price changes and promotions. (An item on sale might be a
good deal that week, but not during other weeks when it is sold at full price.)
·
Next, identify customers who buy mostly
good-value-for-money SKUs, on the assumption
that these are price-sensitive customers who are likely to remember the prices
of the products they buy. The analysis can only be done with loyalty-card data.
Retailers without loyalty-card data can identify price-sensitive baskets
instead of customers, using transaction data.
·
Third, assess the relative importance of items
purchased by price-sensitive customers. This step requires
the retailer to answer two questions about each item: What percentage of
price-sensitive customers buy the item? And what percentage of all the
customers who buy the item are price-sensitive customers? Those two metrics are
then combined and averaged into a price-awareness score.
·
Finally, rank the SKUs, according
to price-awareness scores, within their categories. The top-ranked SKUs are
KVIs.
For retailers whose assortment consists
primarily of infrequently bought items, we recommend a slightly different
methodology that combines three sets of analytics, again using big data (see
sidebar, “Case example: Nonfood retailer”). Each set of analytics helps the
retailer determine which product categories meet the following criteria:
·
Frequently researched online and purchased
fairly regularly (perhaps once every two to three months). The assumption is
that consumers tend to remember the prices of items in such categories. The
data on online prepurchase research is typically sourced from web-analytics
providers such as Google Analytics, whereas the data on actual purchases is
from the retailer’s own transactional data.
·
Expensive or purchased fairly regularly. To
perform this analysis for a category, the retailer needs to calculate average
ticket price and frequency of purchase.
·
Often found in price-sensitive baskets. By
analyzing transaction or loyalty-card data, retailers can determine which
products often appear in baskets alongside other price-sensitive items (that
is, those that meet the first two criteria).
Triangulating the three sets of results
yields a comprehensive list of KVCs. The retailer can then calculate a
price-awareness score—based on frequency of purchase and share of category
sales—for each item in the KVCs. The highest-scoring items are the KVIs.
Practical
advice for implementation
In each case, the results of the analyses
should be commercially validated—that is, category managers and the commercial
team should review and approve the results. Typically, they would evaluate the
KVC list using several lenses. For example, does the category play a strategic
role for the retailer? Is the category a traffic driver or one that typically
triggers additional purchases? (The purchase of a can of paint, for instance,
is likely to trigger purchases of paintbrushes, a ladder, drop cloths, paint
thinner, and so on.) Does the category have one or more highly visible brands? We’ve
found that the most accurate KVC and KVI lists result from a blend of art and
science: category managers’ commercial knowledge and experience, combined with
the rigor of big data analytics.
Implementing these methodologies doesn’t
require expensive new systems or an army of data analysts. We’ve found that
many retailers need just one person with analytical skills to learn how to run
the algorithms and codes. That person can then train the commercial team to
interpret and use the results.
These methodologies have yielded impact across different
types and sizes of retailers in both small and large markets. An Eastern
European grocery chain, for instance, had been trying to beat all of its main
competitors’ prices on almost every item. Since shifting to a KVC- and
KVI-focused pricing strategy, its margins have risen two percentage points.
Similarly, a Western European specialty retailer used these methodologies to
revamp its pricing architecture and achieved a margin increase of 1.5
percentage points.
By Oliver Heinrich, Alberto Mussa, and Stefano Zerbi
http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-improve-price-perception-profitably?cid=analytics-eml-alt-mip-mck-oth-1611
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