The fastest growing ecommerce companies excel at both customer acquisition and retention. In this event, we're going to dig deep into the latter, looking at how other ecommerce companies perform on common retention benchmarks and how to analyze your own data to build a churn-stopping retention strategy.

2. Why Retention Matters

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Top-performers grow significantly faster

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In part due to high acquisition

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Revenue from repeat customers

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Revenue from repeat customers

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Your customers are very different

8. Building a Data-Driven
Retention Strategy

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3 buckets of customers

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3 buckets of customers

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Your data-driven retention strategy
Channel analysis Getting the 2nd purchase Getting the 3rd 4th and 5th
Acquiring more loyal customers Retaining the most valuable customers

12. How can I get more customers
to make a second purchase?

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Getting the second purchase is tough
68% of
customers will
never purchase
a second time.

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Step 1: Benchmark company performance

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+
Step 2: Identify problem areas
Customer Support Data
order #, order date
product #, SKU, size/type
order #, order date
OR
OR

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Product Analysis

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Step 3: Uncover the ideal timeframe for remarketing

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Step 3: Uncover the ideal timeframe for remarketing

19. How can I get a customer to
make a 3rd, 4th, 5th purchase?

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Good news! It gets easier

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Step 1: Evaluate the average time between order

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Step 2: Develop your purchase personas

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Case study: Dollar Shave Club

24. How can I retain my most
valuable customers?

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Step 1: Identify them
$ Spent
VIP customers

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Step 2: Track issues they are having

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Step 3: Discover what they love about you

28. How can I acquire more loyal
customers?

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Step 1: Identify the valuable campaigns/channels

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Step 1: Identify the valuable campaigns/channels

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Step 1: Identify the valuable campaigns/channels

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Step 2: Use your VIPs
Referral
programs
Abandoned
cart offers
Customer
events

33. How can I get to this data?

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Your 3 options
1
Spreadsheets

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Your 3 options
1
Spreadsheets
2
Build a solution

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Elements of an analytics stack
Data Integration
Data Warehouse
BI/Analytics

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Your 3 options
1
Spreadsheets
2
Build a solution
3
Buy a platform

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100% Build Build + Buy 100% Buy
Pros Maximum flexibility High flexibility Some flexibility
Cons Maximum engineering
investment
Moderate engineering
investment
Low engineering
investment
What it is Build API integrations,
warehouse, and analytics
layer
Purchase the various
components that are the
best fit for your business
A complete data stack,
easily configurable
Who is this right
for?
Companies building data
products (Spotify, Uber,
Pinterest)
Companies with 5+
analysts, data scientists
Companies with <5
analysts, little
engineering time
Examples In-house RJMetrics Pipeline +
Looker, Mode, Tableau
RJMetrics CloudBI
Choose your data stack