Identify Your Highest Value Shoppers for Maximum Profitability

MERCHANDISING PLAYBOOK

Identify Your Highest Value Shoppers for Maximum Profitability

Target segment analysis is the process of dividing customers into groups, or cohorts, with similar attributes and purchase behaviors to determine which are the most profitable. Marketing campaigns, product mixes, and recommendations can then be tailored to these specific segments, improving basket sizes, acquisition costs, and lifetime value.

When to consider this play

You need to identify, refine, or expand your target market.

You’re trying to determine or update your ideal product mix or assortment.

You want to make more relevant product recommendations.

Your company runs dozens, hundreds, or even thousands of campaigns across multiple channels and needs to optimize targeting, budget, and messaging.

You need to more accurately forecast profit and predict outcomes.

You’re looking to help maximize shopper retention and lifetime value.

Goals

Determine who your top customers are

Identify common traits and behaviors for your most valuable shoppers

Maximize campaign conversion rates while minimizing acquisition costs

Accurately anticipate and deliver on forecasts

Identify niche or untapped markets

Optimize product mix to attract and convert high-value customers

Increase cross-sells and upsells through better recommendations

Improve customer experiences to nurture loyalty and lifetime value

Play Prep

DATA NEEDED

Point-of-Sale (POS) data – Clover, Square, etc.

Ecommerce app data Shopify, Lightspeed, etc.

Inventory information – QuickBooks, NetSuite, etc.

Website analytics – Google Analytics, Kissmetrics, etc.

CRM data – Salesforce, Pipedrive, etc.

Social media data – Twitter, LinkedIn, etc.

Email analytics – HubSpot, Campaign Monitor, etc.

Ad data – Facebook Ads, Google Ads, etc.

KEY PLAYERS

Merchandising

Inventory management

Data analysis for product/merchandising

Digital marketing

Retail sales

Product manager of growth

Establish a single source of truth

Individual retail tools like eCommerce applications, inventory management platforms, website analytics solutions, and POS systems represent narrow, siloed slices of the customer journey. To get a full picture of customers across each of these touch points and measure impact all the way from first touch through retention, you must have a single, centralized repository where all of your data exists.

Start by asking your data or BI team if you have a cloud data platform (CDP) — also known as a cloud data warehouse — and whether the data sources listed in the Play Prep section of this playbook are being pulled in.


WE DO NOT HAVE A CLOUD DATA PLATFORM

Talk to your BI team about implementing one. Solutions like the Snowflake Data Cloud, Amazon Redshift, and Google Big Query leverage the unlimited scale and speed of the cloud, making it easy to store, manage, and analyze massive volumes of live data from across sources.


WE HAVE CLOUD DATA PLATFORM BUT SOME OF MY DATA SOURCES ARE NOT IN IT

Simply ask your BI team to start integrating your key desired data sources into your cloud data platform so they can more easily be analyzed together to generate insights. Since cloud data platforms are elastic, can handle all types of data, and separate data storage and compute costs, this shouldn’t be an issue. Pre-built data connectors from companies like Fivetran can also help.


WE HAVE A CLOUD DATA PLATFORM AND ALL OF MY DATA SOURCES ARE IN IT

Wonderful — proceed to Step 2.

Collect the right shopper data

To identify and understand your top targets, you need to collect as much information about them as possible as they move through the purchase funnel. Once you have a single source of truth, you can focus on capturing the shopper traits and behaviors your business needs to effectively reach and convert different audience segments.

It’s likely you won’t have all of the data you’d like, but companies like GravyAnalytics and Zoominfo can help enrich datasets to better understand your customers and the shoppers engaging with your marketing initiatives.

Depending on your business model, customers, and campaigns, some types of information are more helpful for segmentation than others:

TYPES OF DATA FOR SEGMENTATION

Demographic – Demographic segmentation is one of the most basic and common types of market segmentation and includes grouping audiences by things like age, sex, marital status, family size, occupation, education level, income, race, nationality and religion.

Psychographic Psychographic segmentations groups audiences by personal motivations, beliefs, lifestyles, personality traits, values, opinions, and interests. This can be helpful if your target market is very large and broad.

Geographic Geographic segmentation is a subdivision of demographic segmentation and groups audiences by city, state, country, zip code, or other regional classifications.

Behavior Behavioral segmentation groups audiences by common behaviors including purchase behaviors, content consumption, engagement, and more.

Want to keep reading?

Learn how to better understand, reach, and convert your top audience segments!