What is Retail Analytics?

I loosely refer to analytics used by retailers or the retail goods
manufacturers to run their operations efficiently. This definition is
inclusive of all departments within a retail organization as well as
across all of the retailers and manufacturers.


For example, the finance group will be more interested in forecasting
the overall revenues and margins for the business based upon past
history. Merchandising division would be more interested in similar
numbers broken out by each of the merchandising categories that they
manage. The catch here is that these analyses may not be consistent
with one another. For example, one would expect that summing up the
merchandisers' forecast would equal that to the finance
dept. forecasts -- but they don't!

Each of these analytics is served by some combination of internal
teams, boutique consulting and other established service providers. We
will look at the important players in another blog post.



How can retail analytics be classified?

We can loosely classify these analytics into three major buckets --
supply side analytics, demand side analytics, and consumer
analytics. Each of them is elaborated below


What are supply side analytics?

Supply side analytics refers to those analytics that affect the supply
of goods, typically related with inventory and supplier management and
other associated activities as explained below.


Inventory replenishment decisions: The main decisions for
inventory management are determining what products to reorder, in
what quantities and at what times. There is a large amount of
downstream uncertainty in terms of merchandising plans, variation
in demand induced by external factors such as weather, overall
economy, holiday periods etc. to make these decisions very challenging.


Initial order quantities (for long lead-time and seasonal
items). This refers primarily to fashion or seasonal goods where
the time from concieving a new design to delivering the final
product is fairly long (6-12 months). To complicate matters, many
of the products in this sector are obsolete at the end of the
season, so getting the initial order quantities is crucial. If
you order too much, the final margins will be squeezed due to the
liquidation pressure towards the end of the season. On the other
hand, under ordering would lead to premature sellout and loss of
additional revenues. Apart from determining the initial order
quantities, this problem could also be controlled downstream by
better design of markdown and promotion events.

Store allocation and replenishment strategies: Frequently, the
finished products are delivered to warehouses or distribution
centers. This is then followed by allocation and delivery over
time to various stores. Roughly speaking, the goal is to stock
the warehouses close to the amount that the stores served by it
are expected to sell. Again, if there's an imbalance it leads to
loss revenues or lower margins due to increased cost of
liquidation or cross selling. The optimization approach deals
with both the upfront stocking of the warehouses, subsequent
decisions about allocating and scheduling of trucks to minimize
various costs.

Other supply side decisions
There are some other decisions taken on the supply side that don't
fall into one neat category. Some of them are outlined below:

Supplier portfolio management: Each business unit (retailer or
the manufacturer) have many dozens of suppliers. These suppliers
are chosen for a number of reasons -- some provide unique
products, others are used to spread the demand, yet others are
used for geographical reasons. There are ways to optimize this
portfolio based upon various criteria such as meeting unexpected
demand spikes quickly, minimizing long term costs and
administrative expenses.

Hedging: Currency, Commodities, etc With the increasing pace of
globalization, it becomes imperative not only to manage the
currency risk, but also the commodity risk. Commodity prices
can fluctuate considerably and lead to unexpected cost increases
in the finished products, so identifying and controlling for the
most important commodities does indeed become very
important. This primarily applies to the manufacturers that
assemble the finished items from the raw materials, but it could
come into play unexpectedly. For example, Southwest airlines had
a significantly lower cost base due to long term crude oil
futures.


What are the demand side analytics?

Demand side analytics closely mirrors the end consumer thinking,
except that it is applied in aggreage across consumers and
geographical regions.

It starts with trying to understand what the consumer needs are (what
is she looking for?) and subsequent pricing and promotion decisions based upon
consumer affordability or the marketplace dynamics. Further, when the
consumer is in the store, how does he navigate the store? How does the
consumer interact with the stores (coupon usage, end-cap displays,
center-store displays, center-aisle displays etc).

Finally, understanding the consumption patterns would help retailers
design their merchandising to promote up-sell and cross-sell
opportunities.



These can then be further divided into following groups:

Assortment optimization: Assortment refers to the collection of
products/services carried by a retailer. In most cases the
fundamental decision that a merchandiser faces is how to change
the assortment to realize better gains - what products to carry? when to
carry them? what locations to carry them?. She is influenced by
various parties -- vendors who want to gain marketshare through a
particular retail channel, internal marketing group that
identifies consumer needs based upon past history, surveys, or
market intelligence. Merchandising strategy of selling private
label brands (e.g. a retailer's own version of cola) vs national
brands (e.g. Coke or Pepsi), internal targets for sales and
profits, consideration of store footprints (The amount and type
of space available vary by the store format and size -- number
and type of aisles in larger stores is more than in the smaller
stores)

Pricing optimization: The most fundamental financial decision is
to set the prices for the products being sold. This has become an
advanced art in itself with various retailers practicing various
strategies -- everyday low prices, Hi-Low prices, dollar-bins
etc. More specifically this refers to setting the everyday price
(or the base price) for fast moving consumer goods (FMCG) and
setting the initial price for the fashion goods. Setting of price
is a very complex process due to the number of SKUs in a category
and the number of stores being served. Also, the optimal price would
vary not only by the geographical region for the store-location, but also
by the assortment carried at the store and prices set by the
competing retailers on similar items. For a national chain with
1000 stores and a medium category of 500 items could have to potentially deal
with 500,000 price variables, each of which could take on up to
100 different price points -- this is where mathematical optimization
plays a lead role in quickly finding the best set of prices.
Promotion optimization: Promotions are typically short term price
reductions in conjunction with the appropriate marketing message
(email message, newspaper flier etc). Determining what subset of
products to promote, how deep to run the promotion, how long to
run a promotion and how frequently to carry out promotions within
a category make this an order of magnitude more difficult than
the pricing decisions. Further, this is a very tactical activity
that requires frequent adjustments. In many cases this also
involves protracted negotiations with the manufacturers.

Clearance/Markdown optimization:
This usually applies to product that are towards the end of their
lifecycle -- either due to normal seasonal changes (swimming
clothes at the end of summer) or due to introduction of newer
products (introducing iphone 4 would cause iphone 3GS to fall
into this category). The primary goal here is to run down the
inventory with the least amount of discounts taken from the price
point. Since there are usually other ways in which the leftover
inventory can be liquidated (shipped back to the manufacturer,
sold to other outlets that specialize in markdown inventory
etc), decision making becomes complicated.

Shelf/Planogram optimization: The theory is that if related
(or frequently co-purchased) items
are located in close vicinity, it is easier for customers to find
them and drive even additional sales (think about the batteries
placed close to many gadgets and toy aisles). Apart from this
consideration, there are other requirements such as carrying all
of the assortment, giving premium placement to some subset of
products, etc. Retailers also think carefully about the facings
-- whether the broader side of the product is visible or just the
much thinner side. An excellent example is the books placed with
the cover page facing consumers or the binding side facing the
consumers.

Display optimization
Apart from stocking the regular shelves, select set of products
can also be placed in other prominent locations. These are
usually located in the high traffic/visibility areas such as at
the end of the aisles (end-cap displays), center of the store
(center-store displays), checkout aisles (checkout displays), in
the middle of the aisles (center-aisle displays). The number of
such fixtures is limited and it also varies by store format -- so
optimally utilizing them leads to higher yields.

Check-out stand optimization (impulse buys)
Although we have mentioned the checkout displays in the previous
section, they deserve separate attention by themselves. They have
the captive audience as customers wait in line and are usually
stocked with impulse buys (candy, magazines, etc) that also
happen to have higher yields.

Feature/Advertisement optimization
Feature or advertisement refers to messaging provided to the
customers. Traditionally this was restricted to the print media
(newspapers, mail flyers, magazines), radio and
television. However, with the advent of the internet this sector
has exploded (or splintered) with the availability of email
messages, paid-search advertisement (Google), social media
advertising, banner advertisement (rich media), coupon sites
etc. Advent of smart phones is fueling a new generation of
capabilities that includes personalized delivery of features
based upon geo-location and other preferences. A newer set of
aveneues have opened up through the placement of audio/video
display devices in stores, at gas stations, in elevators. These
new media differ from the traditional media in one important way
-- advertisement can be targetted more specifically and the
responses can be measured directly in some cases.

What are consumer specific analysis?
Since the demand is driven by an individual consumer, the premise
is that by cultivating better understanding of the consumers,
their habits and preferences, one can cater much better to their
needs and realize bigger benefits. For example, a retailer could
identify that a significant portion of their consumers demand
organic food items, which the retailer is not carrying. In other
cases, such analysis can uncover customer needs that are not served
by a particular retailer (e.g. a customer buying baby food from a
store but not buying diapers).


The Customer Relationship Management (CRM) groups have
traditionally conducted very detailed research to capitalize on
these findings. Some of the major initiatives are listed below.

Determine consumer preferences and tailor offerings
This refers to identifying gaps in assortment (both products and
services) that a substantial group of customers would be interested
in. After analysis, the retailer can decide to selectively expand
in these areas. One great example is the addition of bakery
sections in many of the grocery retail stores. Such analysis can be
key for very seasonal verticles, where catering to changing tastes
of consumers leads to higher sales/margins.



1-1 specialized offers (upsell/cross-sell opportunities)
As one starts conducting this detailed analysis, it is clear to see
that customers are actually quite different from one another --
there are varying levels of brand loyalty, retailer loyalty and
category loyalty. One could influence consumer behavior at the
individual level by identifying the needs and price sensitivity and
providing good value. For example, providing a customized coupon on
bulk purchase -- 10% off of a purchase of $50 or higher in December.



Loyalty and credit card programs
The only way to understand customer behavior is to have access to
detailed transactions over time. This is achieved by enrolling
customers into a loyalty or credit card program (usually a
retailer-branded credit card) since it allows for identification of
customers and the ability to track them over time.

Although the programs are voluntary, it is easy to achieve customer
penetration upwards of 90% by properly incentivizing the
customers. This trove of information is just being mined for
marketing and sales purposes. I should point out that there is some
resistance to this due to privacy concerns, which will hopefully
get resolved to strike a good balance between privacy and allowing
for mining of insights.



Coupon and other offer programs
The whole purpose of doing customer level analysis and marketing is
to influence their behavior. And, price is the most important lever
to influence the behavior. Providing the right coupons for an
individual or a group of customers (large or small) can lead to
increased sales without sacrificing the margins. This could be done
selectively to exclude the occassional shoppers who only
buy on deep discounts.



How does online retailing impact consumer insights?

With internet technologies, not only is it easy to set up an
ecommerce store and serve customers, but it also becomes very easy
to track everything that the customer does -- how frequently does
the customer return to the website? How much time does she spend
browsing before making a purchase? How many different categories
has he browsed? All these learnings can be further used to provide
real-time offers to potential customers -- converting them from
mere browsers into actual purchasers. Social media is probably
going herald a new era of customer marketing that is just about
unfolding now.


Are there any other analytics?

Localization:
Although this covered to some degree in some of the other sections
(assortment, prices), this is important enough to be called out
separately. In a nut shell, the emphasis is on understanding the
differences in consumer behaviour across various stores and
tailoring the regional stores to better serve the constituent
consumers. On the assortment side, this would reflect a wider
variety of ethnic (or special interest) items to reflect the local
population. Pricing and promotions also need to be tailored
specificially to reflect consumer choices (e.g. senior discount
days, or, promotion of canned beans around Cinco-de-Mayo)



Store Location & Operations
Retailers are continuously evaluating their needs in terms of
opening or closing stores, expanding or remodeling of existing
stores. These are large capital expenditures, so getting an
accurate read on the future demand based upon a possible decision
is very useful. For example, if a retailer wants to open 5 new
stores, then they can find optimal locations of opening the stores
based upon demographic information, market data, competitor
activity etc. Similar analysis would be helfpul in determining
which stores should add a new department (a bakery, a florists
stand etc).

Operations refers to streamlining and optimizing the store operations
-- for example how and when to restock shelves, how to design process
for efficiency. This is mentioned more for completion than any
specific analytics application.