Tom Tailor: How to Encourage Offline Customers to Buy Online

Originating from Hamburg, Tom Tailor sell casual clothes all over the world. The company was established in 1962, and its total revenue reached more than $630 million in 2019.
Company size. 
57 stores in the CIS and 1 million leads in the customer database
Elena Lozhkina,
Head of Marketing and E-commerce at Tom Tailor
Convert offline customers into online shoppers
Boost revenue shares through direct marketing
Launching an omnichannel loyalty programAdding item returns, reservations, and try-on options in-store and launching other steps to encourage customers who buy offline to buy online insteadLaunching targeted and behavior-based trigger campaigns for both online and offline customers

IT stack. 

CRM and Cash Desk, website, Mindbox
Revenue generated by identified customers from campaigns reached 8.93% of the total revenue from online and offline sourcesRevenue from triggered campaigns increased threefold (based on last-click attribution)54 new triggers
The following case study is from Mindbox, the original brand behind Maestra’s technology
While supporting its long-running and well-established retail chain, Tom Tailor continues to develop its e-commerce marketplace. The brand’s goals are to increase its share of online sales in total revenue and improve the omnichannel customer experience. Encouraging offline customers to buy online instead of offline is an effective way to achieve these goals. Elena Lozhkina from Tom Tailor, and Yulia Somova-Goltsvirt from Kokoc Group’s Profitator Agency have shared their experience regarding:
  • Why offline and online customer bases should be combined and how the omnichannel loyalty program plays a part.
  • Why the 54 or even 100 triggers introduced don’t feel like spam.
  • What would be more attractive to customers than discounts with insights from our hypothesis testing.
  • When offline customers will be ready to switch over to online, if at all possible.
  • What issues retail customers have that may stop them from buying online.

Results of Marketing Automation and the Omnichannel Loyalty Program

Revenue from campaigns attributed to identified sales vs. the total revenue of the online store and the retail chain.

Data from the summary report generated on the campagins launched with Mindbox.

Revenue from tailor-made trigger workflows:

Data from Google Analytics (last click attribution)
  • 54
    triggers were activated
  • 8.93%
    was the maximum share of revenue from direct communications (attributed to identified sales) compared to the total 9-month revenue
  • 3
    times more revenue from last-click trigger campaigns

Supporting the Decision to Automate Marketing

Brick-and-mortar stores and the online store operated separately. Because the marketing activities and metrics were different, it was too complicated to combine the two channels. Therefore, no single approach to customers could be applied.
To align online and offline databases with the marketing automation platform, offer a single management tool for both and create reports for all the commands in only one window.
Online and offline promotions had their own unique rules, making promotion combinations and terms difficult to understand.
To launch a loyalty program that tracks online and offline behavior of customers and syncs promotions for both.
Revenue growth had slowed because there was no process to test new ideas and launch marketing campaigns.
To use the platform to launch and test new ideas and boost revenues from new marketing campaigns.

Implementing the Platform

It took 8 months to implement the platform, and launch the online and offline loyalty programs, triggers and new campaigns. All the integrations were performed in parallel so that Tom Tailor could use the platform features as soon as possible without slowing down business.
Specifics Considered During Integration:
Customers’ bonus points and statuses had to be transferred from the current loyalty program. Tom Tailor’s current loyalty program used to run on a legacy system. To retain the bonus points and statuses, the team integrated the customers’ personal accounts along with POS and website data. It took a few weeks to prepare for this data transfer, and the team worked on it at night to make sure operations still ran smoothly during the day.
Independent promotions for the online and retail channels. Items and discounts can be different in the online and offline channels. The loyalty program had to account for these differences as well as about 50 promotions that had already been running against the recently made rules. The new loyalty program was aligned to cover those promotions so they were ready at launch.
Click-and-collect orders with segmented items within a single payment. With the click-and-collect model, a customer can order online and pay at a brick-and-mortar store. By default, the platform recorded orders like this as online ones attributed to the point of order placement. If a customer bought extra items at the “collect” store, their online promotion would not be applied to these extra items. This was seen as a problem. So a special feature was developed to separate items in the receipt. It would apply online promotions to an original order and then apply offline promotions to the cross-sale items. The platform now marks items like this specifically to ensure that BI gets the correct information by points of sale and promotions.
Omnichannel features for customers. Mindbox is the master database for contacts and bonus points. However, it stores only bonus points for retail customers, whereas contact details are stored by the website. For this reason the online and offline bases could have different details for the same customer. To fix this issue, a feature was added. If a customer changes their data at a brick-and-mortar store, once online, they get a pop-up message asking them if they’d like to update their data on the website as well.
Testing. There were extra subcontractors to combine the online and offline databases together. The results needed control and testing, which again was an additional workload for the team.

Activating the Triggers

  • 1
    campaign per customer every 2 days is the highest possible limit for automated campaigns to ensure communication doesn't feel like spam
Custom trigger workflows were used to automate communications with customers and plan as many engagement scenarios as needed. The first task was to automate communication with clients and create workflows that foster the highest level of interaction possible — automated campaigns helped with this. In total, 54 trigger-based campaigns were activated, with a plan of activating 100 by the end of 2021.
A marketer works on fitting the workflow logic into the communication map and then sets it up in the platform. Priorities help avoid spamming customers, while at the same time supporting a large pool of different triggers. So, if an abandoned cart sequence is enabled, the other triggers will then be disabled. As another example, customers that are in the process of ordering or with a recent history of purchasing will not receive promotions. This rule, however, does not apply to transactional and service messages.
Let’s take a closer look at our most noteworthy campaigns:
Seasonal campaigns make selections based on the weather, winter or summer categories, or a favorite category. Today, campaigns like this address narrower product categories such as textiles, denim, outerwear or accessories. Next, we plan to cover all categories to boost revenue across the board from these types of campaigns.
An automated workflow will generate any seasonal campaigns with catalog items and send emails when scheduled or when an event occurs
Seasonal campaigns are segmented by gender. The loyalty program collects data and awards bonus points for filling in data sheets. Customers can spend points both online and offline
Cost-Based Selections. At Tom Tailor, the average order value is $50. To develop the most suitable offer for customers, cost-based selections were created and then split into two categories: items that cost up to $30 and items that cost up to $50. Interestingly, the order conversion rate for both campaigns proved to be twice as high as that of promotional campaigns. This method of categorization saves resources that may have been spent on discounts and, in turn, keeps profits high.
The marketer can initiate an automatic selection that takes into account gender specifications and price of goods. For example, a selection can be made for items close to the average order value, without cheaper accessories or items below the average order value. These types of campaigns will reactivate inactive customers, while active ones or those with much higher or lower order values will not get these types of messages.
Cost-based selections help reactivate inactive customers and avoid issuing unnecessary discounts. Cost-based campaigns have double the conversion rate of promotional campaigns with discounts
  • ×6
    six times higher conversion rate for orders from loyalty level upgrade campaigns compared to abandoned cart campaigns
Leveling Up in the Loyalty Program. A customer receives a message letting them know how much they need to spend on their next order to upgrade their level in the loyalty program and invites them to make a purchase. These emails proved to bring the highest revenue per email (RPE). It’s also worth mentioning here that the conversion rate outscored that of the abandoned cart, which had previously shown the best conversion rate.
The loyalty program upgrades a customer’s status based on the total value of their purchases. A message that informs the customer of the amount left until the next level is a good tactic to increase the customer’s desire to place an order

Testing Hypotheses

To determine the most effective types of communications and maximize customer engagement, we test our hypotheses with A/B testing.
  • Bonus points are better than discounts:
    conversion rate for orders

Recovering an abandoned cart: bonus points vs. discounts

We launched a trigger-based email sequence for abandoned carts. The first message would remind the customer of the items they added to their cart and the second one would offer bonus points or a discount, encouraging customers to complete the orders they had previously abandoned. We split the discounts vs bonus points tests between two groups to better understand exactly which feature works best. Group A was offered 500 bonus points and group B was offered a 5 percent discount. The reliability rate of the test was 95 percent, according to last click attribution.
  • ×1.3
    higher click rate
The test results showed higher conversion and click rates for the bonus points compared to the discount. This was a surprising outcome, since the bonus points can only cover up to 50 percent of an order. Applying the discount to a regular priced purchase/cart is actually more cost-efficient for the customer.

Relevant Item Selections vs Selections Covered by Bonus Points

Next we tested what encourages customers to spend their bonus points on their next purchase. For this, we compared 2 types of emails:
  • Email A: A message with the amount of earned bonus points and a CTA to buy items selected using the customer’s preferences.
  • Email B: Only items which did not cost more than the amount of bonus points that that the customer had collected.
The reliability of the test was 95 percent, based on last click attribution.
The test revealed that personalization with a thoughtful approach towards our customers works the best. Group B showed higher click rates and a 7 times higher revenue boost.
To understand what kinds of messages are more engaging, we compared personalized selections of relevant items with those covered by bonus points. Surprisingly, the selections covered by bonus points were more effective

Addressing Customer Concerns for Offline to Online Migration

Once we had the capability to conjoin the online and offline customer bases and track customers’ actions in both channels, we were able to learn more about the behaviors of retail customers. It helped us to understand the reasons why customers chose not to buy online, namely:
  • 80 percent were afraid of choosing the wrong size and having to deal with returns
  • 50 percent were not aware of the try-on option for online stores
  • 30 percent had never shopped online because they could not return items offline
We addressed each concern respectively. We have added order returns to brick-and-mortar stores and a free shipping option for some loyal customers. We then extended the return period to 365 days. Finally, we informed customers of the try-on option for our online stores. We also launched targeted advertising for offline customers to tell them about the aforementioned advantages.

Developing the Omnichannel Customer Experience

Mindbox Product
Loyalty Program

We set a goal of doubling the share of omnichannel customers. To achieve that, we first analyzed the database. We took customer segments with an active email subscription, estimated how many of them are customers with omnichannel orders, and analyzed the actions of those who buy only offline. We learned that customers do read our messages but still prefer brick-and-mortar stores. To try changing their minds, we developed and tested a few promotions:
A 50% discount on the first order as a motivator.
We chose a sample of retail customers who had visited our website and sent them messages offering a 50% discount on their first online order. We then compared those results with a control group.
The total amount of orders placed was 73, with 62 of them being online and 11 of them offline purchases. The conversion rate of this campaign amounted to 0.4%. We realized that converting customers from offline to online buyers was possible, but some customers would always prefer to buy offline, no matter the discount offered to them.
A 50% discount promotion to attract retail customers to buy online

Click and Collect

The next major step was to implement the click-and-collect system, allowing customers to place orders online and try them on and pay in-store.
The click-and-collect system helps retail customers to buy online while still sticking to their usual purchases

Retail Customers Migrating Online. Example:

Migrating customers from offline to online is a long and continuous journey. The click-and-collect system may prove to be a crucial factor in helping to facilitate that journey. Below is an example of a real customer’s offline to online migration timeline:
2018. The customer buys items at retail stores. They are a regular retail customer with a rich purchase history.
January 2021. The customer joins the loyalty program at an offline POS and begins to receive personalized trigger messages:
February 2021. The customer opens their messages but continues buying offline:
The customer begins to visit our website from email notifications:
April 2021. Authorized on the website for the first time:
The first “click-and-collect” order. Thereafter, the customer placed two additional orders. This implies that the customer clearly enjoys the service and buying online:
The Omnichannel Customer Base Growth: Results
The share of omnichannel customers with specified emails in the customer database. As new communication means and campaigns are introduced, the omnichannel share grows slowly, but steadily

Why We Decided to Update the Loyalty Program Rules

At first, the loyalty program rules divided customers into two segments: Standard and VIP. 95% of customers belonged to the standard segment and 5% belonged to the VIP segment. VIP customers were the minority and were actively buying without any assistance or influence from us. However, the standard customers were rather inactive, wherein we experienced customer churn. So, the VIP baseline proved to be too high. To lower this line and create a new means of communication, we extended the program to include a third active tier.
To estimate loyalty program productivity, we calculated the retention rate after some considerable time from the program’s debut and compare it with a control group.
The rules of the loyalty program
Three loyalty program levels. The customer’s level depends on the total value of their purchases. Earned bonus points do not expire. However, a customer still has to spend a certain amount annually to retain their status.