1. The Omnichannel Customer Omnichannel data
Module 5 Video 5
The Omnichannel Customer
Omnichannel data

2. Intro
Retailers have a wealth of data that they can use to make better decisions
In an omnichannel context, customers interact with the retailer in many different ways, and this leaves tons of data behind.
This data can be very valuable
Also hard to analyze.
Types of data that are generated in an omnichannel context
Customer-level
Internally at the company
Examples about things retailers can do with that type of data
Challenges associated with omnichannel data

3. Customer-level data
Purchase journey involves interactions through different contact points
Generates many different types of data
Example:
There’s transaction sales data from online and brick and mortar stores
There’s also traffic data, both in stores and online
There’s social media data such as likes and comments
Detailed session data such as clickstream data, or data about shopping cart abandonment
Captures instances when a customer puts products on the shopping cart, but does not complete the purchase.

4. Customer-level data cont.
Tracking data across different channels can be hard
Difficult to match offline and online data for the same customer
It would be very valuable to the company to be able to track that information.
For example, they could use that to offer more effective promotions or better targeting.
A way retailers can try to link the information of multiple interaction
Loyalty cards
External data sources that help retailers connect transactions with customers.
Also valuable for the customer
For example, wouldn’t it be nice if at Nordstrom.com, I could see my order history, including purchases that I made at one of their brick and mortar stores?
At WarbyParker.com I can actually see all my orders, irrespective of whether they were placed at the store or from home. Warby Parker even asks customers in the store for an address and then sends a message with the products that the customer has been viewing in the store, so that they can complete their transaction later on if they wish to do so.

5. Backoffice data. real-time inventory accuracy
An omnichannel environment puts pressure on the internal data of the company
Inventory data
Research has shown that retailers often have inaccurate inventory records, and this can be an impediment to omnichannel retail.
If I provide customers with information about the availability of products at the store, this information has to be reliable.
If I want to give my customer a reliable estimate of how soon he’ll receive a product he’s about to order, I need to know where I am going to fulfill that order from, so that I can give a shipping estimate.

6. Backoffice data. real-time inventory accuracy cont.
Requires keeping very good track of the inventory at the different locations
More difficult to keep accurate track of inventory at stores than at distribution centers
Customers are directly interacting with the inventory at the stores. For example, they may misplace the items and they may be hard to find.
Retailers have to invest on IT, forecasting, and resorting if they want to track inventory data across multiple channels.
They will also have to invest on processes to make data more accurate

7. Examples. Using data Anthropologie tracks online cart abandonment data
Sends s to customers with abandoned carts with a direct link to the checkout process and with additional product pairing suggestions.
These s remind customers about the brand and the products they had selected, and increase the likelihood of purchase.

8. Examples. Using data cont.
Improve operational decisions of the retailers
We worked with an apparel company and tracked customer engagement at their social media sites through likes and comments.
We then used this information to enrich a forecasting model.
We found that using this type of data improved the sales forecast
There are multiple opportunities to improve operational decisions using customer data.
Online traffic data can be geolocated
We know where our customers visit us from
We can use that data to decide where to locate a store, or to customize the assortments of a particular region

9. Challenges of omnichannel data
Omnichannel data provides challenges
One such challenge is the “attribution problem”
In an omnichannel context, it is harder to assess the impact of different actions that may be costly.
For example, suppose I want to decide how much to bid on an ad at google.
To make such decision,
I would like to understand how showing that ad will affect present and future sales
With the increasingly complex purchasing journey that customers follow, this is very difficult.
A customer may see the ad online, but decide to go to the store and purchase the product there. In this situation, it would be difficult to attribute this outcome to the decision of showing the ad.

10. Challenges of omnichannel data cont.
Another challenge is that different units of the company may only have access to partial data
Online branch of the company and the stores have their own internal data and metrics that they track over time.
Relying on single-channel data ignores the important but often subtle interactions between channels.
Important to look at the data across all the channels
When analyzing data, we should take the perspective of the company, and not the perspective of one of its parts.
For example, in our work with a homeware retailer, we wanted to study the impact of a new program.
After the implementation, the online team saw a decrease in online conversion.
Based on their own internal data, they thought the program had been a bad idea and they proposed to shut it down.
However, after inspecting the evolution of sales at the stores, it became evident that the new program increased sales at the stores, and the net effect for the company was positive.