Sample Analysis

Below are five examples of customer journey analysis you can run on our platform. Click to see how to build the journey and what you can learn from the dashboard.

1. Which content drives conversions
2. Maximizing AOV via acquisition channel
3. Promo codes affecting LTV
4. Promo codes and refunds
5. Customer satisfaction driving LTV

Or watch a demo of our analysis below...
Which content drives conversions

Scenario: a retailer has content targeting high-end products and low-end products. Does the content impact conversions? If so, how?

We create three separate conversion paths. One for high-end content, one for low-end content, and one for all pages — this last one is meant to be a control group.
We connect each page view to a transaction to see how often that specific type of page view converts.
The analysis shows us that high-end content converts better — 53% versus 34%. On top of that, low-end content takes about 16.5 hours to convert, while high-end content takes just over 2 hours for conversions... Not only do we see more conversions, but they happen faster, too!

 

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Maximizing AOV via acquisition channel

Scenario: maximizing Average Order Value (AOV) is critical for ecommerce success. Which channels and acquisition tactics drive higher AOV?

In this case, we are testing two types of strategies: Tiktok ads and special offers for a first purchase.
We split orders (i.e., transactions) into two types — those with a basket size above $60 and those below $60.
Our analysis shows that special offers convert with the same rates for low and high AOV transactions. Tiktok, on the other hand, generates 3.5x as many conversions (14.8% versus 4.2%) for transactions above $60.
We add a second transaction to see if there are any longer-term effects. Both high- and low-AOV transactions have the same conversion rate to second purchases. In both cases, the conversion rate is about 35%.

 

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Promo codes affecting LTV

Scenario: our ecommerce team is testing three types of promo codes: 5% off, 10%, and 25% off your first purchase. How do these promo codes affect first purchase conversion and long-term purchase frequency?

We start by creating three purchase events, one for each promo code. The promo code is set in the "Event Settings" box in our platform.
Since we're interested in tracking how conversions take place over the long run, we add a second and third purchase component to the journey after the purchase made using the promo code.
We can already see that the conversion rate from first purchase to second purchase is very different for the 25% off promo code — it is nearly half of what the others are! Clearly, the long-term prospects of a 25% off promo code are not good.
The good news is that the conversion rates after the second purchase become the same across the board.

 

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Promo codes and refunds

Scenario: suppose our ecommerce team from the scenario above is also interested in seeing how promo codes affect refunds. Do promo codes affect refunds in any way?

We add a refund event to the journeys we drew in our earlier scenario. Now when we run our analysis, we'll see how promo code transactions convert into refunds.
The analysis shows that our 25% promo code has over 8 times the refund rate as our 10% promo code, which has the lowest refund rate! Clearly, the incentives of the 25% off promotional code are not leading to the relationships we want.

 

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Customer satisfaction driving LTV

Scenario: the question of whether customer satisfaction drives LTV is an age-old and sometimes controversial one. With DataCX, it's easy to see the impact of customer satisfaction on purchasing patterns. Does customer satisfcation — in this case whether the customer is a promoter or detractor of the brand — affect follow-on purchases and LTV?

We begin by building a journey starting with a page view, then a first transaction, and then a survey question that splits the path into those of promoters and detractors. Finally, we link the survey question back up to see how the conversions from each respondent impact follow-on purchases.
Our analysis first shows that there are significantly more detractors than promoters. Worryingly, 21.87% of purchasers become detractors, and only 6.5% become promoters.
More problematically, detractors have a significantly lower conversion rate to follow-on purchasers! Only 19.09% go on to make a second purchase, versus 54.7% of promoters.

 

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