Multi- and Cross-retailer Analyses
Building a system you can rely on
The opportunity
Big-impact opportunities to improve your bottom line often emerge when (a) you roll up your sales and
supply chain data from all your retailers for a given product, product line or category to see its overall
performance, and (b) you compare your sales and supply chain activity from one retailer partner to
another.
Multi-retailer rollups are, of course, essential for gaining insights into
product and category performance at the enterprise level that can drive
future development strategies. But beyond that, they also permit you to,
in effect, create your own market data. And this market is often more
helpful in understanding marketplace trends than third-party data sets
in that they are assembled and combined from the ground up in ways
that are directly relevant to your mission.
The best strategic view
of your marketplace may
come from creating your
own market data. Why?
It’s perfectly aligned with
your business objectives
and strategies.
Cross-retailer analyses involve comparisons, and help ensure that
profitable tactics and strategies arising from one segment of your market
get visibility for use in others. You may, for example, find products selling well at retailers A and B that
aren’t even carried at C, or affinity-selling tactics that worked at retailer B that you haven’t tried at C. Not
only have you identified some low-hanging fruit, but when you approach retailer C, you’ll have the data to
back up your proposals.
The obstacles
The biggest impediment suppliers face in trying to perform this kind of multi-retailer analysis is the lack
of a strategic, consistent approach to acquiring and processing retailer data. For some suppliers, retail
data collection even from a single retailer is a lengthy and ad-hoc process that produces inconsistent
results from week to week and month to month, and it often doesn’t yield actionable insights until long
after the fact. Other suppliers have developed spreadsheet-based solutions for some of their retailer data,
so while they may be getting their analytics done within the week, the reality that each of their offices or
retailer groups uses their own variation on the system means there’s no way to accurately compare these
analytics across teams.
These problems of consistency and timeliness only get worse as suppliers raise their level of analysis to
incorporate even more retailer data points, additional retailer data streams and third party data.
The solution
Suppliers can go a long way toward overcoming these obstacles and start enjoying the benefits of multiand cross-retailer analyses through proper planning and a focus on achieving consistency. This solution
applies whether you are building your own system or implementing a third-party multi-retailer platform.
Step 1: Designate a Data Czar
The first step is to designate a Data Czar (person or team) that will lead this effort company-wide. This
czar should understand at least the basics of data acquisition and processing both from a technical and a
retail perspective so they can communicate effectively with
A robust multi-retailer system has both
both your IT people and your retail teams.
Once established, the office of the Data Czar can then focus
on the next three steps. For each step, the business needs and
the IT implementation should be worked out in concert, with
both “sides” contributing creatively to develop a system that
is practical and effective.
an IT component (understanding the
data streams) and a retail component
(understanding the business needs). A
good Data Czar will feel comfortable in
both worlds and negotiate a productive
meeting of the minds.
- CPG company vice-president
Note: if your company is in the early stages of developing a
retailer data acquisition system, it can be very helpful to start
by focusing in on a few key metrics or data points and working through the entire process with them
before trying to take on all available data streams.
Step 2: Acquire the right data (for you)
Take a top-down, strategic look at what metrics are key to making good decisions for your specific busines
goals. Then be proactive in requesting that specific data set from each retailer (what you need may not be
what’s in their “standard package”).
Once the key metrics are established, the Data Czar will have to work with IT and each retailer to
determine how the data will be acquired: whether it is acquired directly or through a third party, what
format it will arrive in, how you will extract the data you want from each retailer feed, and so on.
Step 3: Normalize the data
Each retailer has their own way of capturing data and calculating “standard” values. Just because the
data feed from retailer A has fields identified as “Last week’s sales” or “Average Weekly Sales,” there is no
guarantee that data with the same name from Retailer B is comparable. Examples: Some retailers include
coupons in the sales price they transmit and some don’t; other retailers may not collect data over the
same exact time period (not all “weeks” or “seasons” are equal).
The key to normalizing the data is to create your own standard data definitions for all the key metrics you
developed under Step 1, then come up with a translation matrix to normalize each retailer’s data stream
to your data definitions. In theory, this is a one-time headache (per retailer), but the Data Czar will need
to stay on top of this, because things change both internally and externally, and the external changes often
occur without much warning.
Another key to success here is to avoid rendering your history data useless because of changes in the
internal or retailer landscape. Common situations that can cause inadvertent losses of valuable history
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Multi-retailer Analyses: Big picture means big profits
include SKU changes when products change or become “promotion” specials, retailers making changes in
their SKUs and UPC codes for the same product, retailers making changes to store location data or change
store formats, and retailers being acquired by a competitor or conglomerate and converting the new
owner’s product identification systems. Part of the Data Czar’s job is to make the appropriate adjustments
in the translation matrix so that history is preserved as much as possible through all these changes.
Step 4: Attribute the data
Now that you have good data that’s comparable across retailers, you want the flexibility to analyze it
in multiple dimensions because you never know where the low-hanging fruit is going to show up. This
means having a robust set of store and product attributes applied to the data. Once again, best practices
say that attribute lists should be internally developed based on your company’s specific business needs.
It’s easy to simply adopt a major retailer’s attribute lists, but the retailer’s idea of what’s helpful may
not be what’s most helpful to your business. Once the attribute tables are established, the Data Czar will
need to develop and maintain a translation/application protocol that takes the incoming data and either
attributes it from scratch or converts the retailer’s attribute codes to your own.
Conclusion
This kind of well-planned and well-executed plan for data acquisition and processing will work wonders
for improving the consistency and timeliness of your reports, and make possible the many benefits of
accurate multi-retailer and cross-retailer analyses.
Creating a robust multi-retailer
demand signal repository.
Key steps:
1. Appoint a Data Czar
○○ IT and retail knowledgeable
○○ Ensures consistency across enterprise
2. Acquire the right data
○○ Strategic metrics for your business
○○ Work with retailers to go beyond their
standard data offering
3. Normalize the data streams
○○ Create your own mission-focused
definitions for common data points
○○ Translate their data to meet yours
4. Build strong attribute tables
○○ The engine of powerful insights
○○ Helps with consistent reporting across
enterprise
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Multi-retailer Analyses: Big picture means big profits
Retail Data Intelligence
Big Data. Big insights.
About
SetSight’s native cloud-based data-and-analytics platform helps you mine today’s big retail data streams
for the insights you need to enhance your retailer relationships and maximize your profits. On the front end, SetSight is an insights-driven platform that puts actionable analytics and reporting for
sales, supply chain and category management at your fingertips, with presentation-quality formats that
are clear, easy to use and meeting-ready. On the back end, it removes nearly all of the headaches associated with big data acquisition, processing
and storage. It does the heavy data lifting so you can get right to work. All of this is backed by the most responsive customer service and client-friendly contracts in the business. Insights Program
SetSight’s Insights Program was created to provide you and your team with information and insights
that help you navigate the complex business of growing your profits through optimizing your retailer
relationships. Our Insights Program has multiple elements:
•• Insights\Briefing: Bi-monthly executive briefs (like this one) discussing industry-level trends and the
opportunities they provide to the agile supplier.
•• Insights\Manager (coming later this year): Monthly newsletters and an ongoing blog presenting
forward-thinking tactics for optimizing retailer relationships.
•• Insights\User: Monthly newsletters and an ongoing blog helping all current users of the SetSight
platform leverage their SetSight investment to the fullest.
Proof of Concept Demo
SetSight uniquely provides a robust Proof of Concept Demo where your actual sales and inventory data
from one of your retailer partners is loaded into our system for a trial run. This demo will show the range
and depth of our platform’s analytics and reporting using products you know well, and will demonstrate
its power to provide actionable insights that will enhance your top-line revenues. The demo is free, and
the insights are yours to keep and use.
For more information call us, email us or see the “Get Started” page of our website.
www.setsight.com
[email protected]
(800) 490.0424