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Past Analytics Excellence Awards

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Authors: Irina Mihai (@irina_mihai) , Web Analyst 

                  Johannes Wagner, Senior Business Analyst

Company: Adidas International Trading B.V.

 

Awards Category: Name Your Own - Creating the New

 

Describe the problem you needed to solve 

The ecommerce business division was facing the challenge of keeping track and steering the performance of over 9000 articles.

 

Senior management had an overview of top level numbers but the actual people who could take action and steer the business on operational level had limited information.

 

Merchandizers tracked the sales of only most important product franchises which generated roughly 60% of the business, but they did not have an overview of article size availability and warehouse stock which was vital in order to know whether getting more online traffic for the article would lead to more sales or actually disappointed customers who didn't find their size. Besides stock information, merchandizers also needed BI data and web analytics data in order to have a holistic understanding of article and franchise performance, a situation which caused delays in acting upon information and steering the business proactively.

 

Even so, the full product range and especially the low-key franchises (40% of the business) were reported on an ad-hoc basis. No actions were taken on the less important franchises which led to unrealized opportunities, as unsold products are heavily discounted at the end of the season.

 

Given this complex business environment and time needed to get hold of data which even becomes obsolete before reaching the relevant stakeholders in a digestible format, we needed to give transparency on all product franchises and provide all the relevant information needed to take actions and drive the business on both aggregated and granular level, in real time, in one place, available to everyone, in an automated way.

 

To sum up, the drivers that led to a new way of working within analytics were:

 

  • Tracking ongoing performance on all articles improves our margin so that we can drive sales during the season and avoid heavy discounting at the end of the season. Offering too many discounts also has a negative long-term impact on the brand and educates consumers to buy on discount, so we wanted to make sure we maximize opportunities within season.
  • Besides immediate financial returns, we are also thinking of the consumer experience and the fact that not finding their desired sizes online disappoints customers. Being able to drive demand planning proactively and ensure enough supply is available is a way to keep customers happy and returning to our site.

 

Describe the working solution

Alteryx has allowed us to tap into multiple sources of data in a fast, scalable way not possible before, which allows us to be truly agile and data driven as an organization.

 

On a high level, the data sources used in the workflow are:

  • BI data incl. sales data and standard margin per article per day
  • Waiting List data from the CRM system indicating the number of times an out of stock product was placed on the waiting list
  • Article master data from the range management application
  • Demand planning master data with the estimated bought quantity per size which defines the relative importance of each size of an article
  • Web analytics data for product views and conversion rate  
  • Stock quantity data from the online platform with the daily stock snapshot on size level
  • Product range files manually maintained for retail intro date,  marketing campaign information, and original sales forecast quantity per month

 

  1. There are 3 work streams used in the main workflow:
    1.1 Calculation of daily sales forecasts per article number based on the product range files and master data file.

Several operations are done to clean up the data but the most important part is transforming the monthly forecast to a daily level also taking into account the retail intro date. For example if an article has a retail intro date in the middle of the month, we only generate a forecast for the days after that date and not before, to maintain accuracy.

 

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1.2 Data cleanse operations done on web analytics and BI data and subsequent join on article and day level

 

For each data type we have created a historical Alteryx database that gets unioned with new cleansed data, which then gets written into the historical database.

 

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1.3 Join of the daily sales forecast with the web analytics data, BI data and wishlist data on article and day level

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Here we also calculate the actual retail intro date for each article based on the first day when the product gets online traffic, thus allowing us visibility on products that were launched late.

 

  1. In a second workflow we calculate the stock availability per article size and size and buy availability per article. This is based on the master data file indicating the buy percentage per size and article and stock snapshot indicating the size availability per article. The output is a Tableau data extract.

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The outputs of the two workflows are then visualized in a Tableau dashboard that has a flow-like structure allowing users to see performance of the product franchises on high level and also drill down into details on article level:

 

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Describe the benefits you have achieved

First of all, without Alteryx the Trading Dashboard would not have been possible due to the sheer amount of data sitting in different systems and manual work involved in retrieving and combining it at the same level of granularity.

Alteryx has allowed us the possibility to blend a variety of data sources in a scalable way and achieve the following business benefits:

 

  • In terms of time savings, prior to using Alteryx, two full time employees would have been needed to compile an in-season daily snapshot of the most important product franchises (60% of the business) with all the relevant metrics. By the time this report reached stakeholders, the information would have been obsolete and irrelevant to quickly react to consumer behavior in real time. Now with the help of Alteryx it takes 10 minutes per day for the analytics team to provide a holistic dashboard to both senior management and employees who can take quick decisions and steer the business based on real-time data.
  • Increased revenue and margin optimization: Our merchandisers and category managers now have a daily complete overview of how each and every single article is performing. Due to the exploratory and intuitive nature of the dashboard (from top level to detailed article level and coloring based on forecast achievement) they can easily identify which product franchises and individual products are falling behind sales forecast and what specific levers to pull in order to increase sales. Example actions are driving more traffic, improving on-site merchandising, restocking particular sizes, decreasing the price.
  • Customer satisfaction: as sizes are restocked faster than before due to the new proactive way of working of the demand planning department, consumers are also happier that they can purchase their desired sizes. This leads to more customers returning to our site because they know here they can find sizes that are not available in retail stores.

 

We have recently introduced the Trading Dashboard and there is already a mindset shift happening where different departments work more closely together to identify opportunities and act based on the data. We believe Alteryx has enabled us to reach our ambitious growth targets, improve customer satisfaction and operate as a data driven organization.

 

Author: Thomas Ayme, Manager, Business Analytics

Company: Adidas International Trading B.V

 

Awards Category: Name Your Own - Best Planning and Operational Use

 

Describe the problem you needed to solve 

As a new and successful business adidas Western Europe eCommerce keeps on growing faster and faster; new services are being launched every week, an increasing number of marketing campaigns are being driven simultaneously, etc. This leads to more and more products having to be shipped out every day to our end consumers.

 

This strong growth leads to an exponential increase of the complexities when it comes to forecasting our units and orders volumes, but also to bigger costs in case of forecasting mistakes or inaccuracies.

 

As these outbound volumes keep on increasing, we were being faced with the need to develop a new, more accurate, more detailed and more flexible operational forecasting tool.

 

Such a forecasting tool would have to cater to the complexities of having to forecast for 17 different markets rather than a single pan European entity. Indeed, warehouse operations and customer service depend on a country level forecast to plan carriers and linguistic staff. This is a very unique situation where on top of having a rapidly growing business we have to take into account local marketing events and markets specificities.

 

Finally, given the importance of ensuring consumer satisfaction through timely delivery of their orders, we also decided to provide a daily forecast for all 17 markets rather than the usual weekly format. Such a level of details improves the warehouse's shipping speed but also increase once again the difficulty of our task.

 

Describe the working solution

 

Our first challenge was to find reliable sources of information. Both business analytics (financial and historical sales data) and web analytics (traffic information) data were already available to us through SAP HANA and Adobe Analytics. However, none of our databases were capturing in a single place all information related to marketing campaigns, project launches, events, adhoc issues, etc.

 

That is why we started by building a centralized knowledge database, which contains all past and planned events that can impact our sales and outbound volumes.

 

This tool is based on an Alteryx workflow, which cleans and blends together all the different calendars used by the other eCommerce teams. In the past, bringing those files together was a struggle since some of them are based on Excel while others are on Google Sheets, moreover, all are using a different format.

 

Workflow Knowledge database.jpg

 

We made the best of this opportunity of now having a centralized event database by also developing a self-service visualization tool in Tableau, which displays all those past and future events. Such a dashboard is now used to:

 

  1. Give some background to our stakeholders about what is driving the volumes seen in the forecast.
  2. Have an overview of the business during our review the sales targets of the coming weeks, etc...

 

In a second time we created a workflow, which thanks to this new centralized event database, defines for each past and upcoming days as well as for each markets a set of "genes". These genes flag potential adhoc issues, commercial activations, level of discount, newsletter send outs, etc.

 

This gene system can then be used to define the histoical data to be used to forecast upcoming periods, by matching future and past days that share the same or at least similar genes. This is seen as the first pillar of our forecasting model.

 

The second pillar of our forecasting tool is a file containing our European weekly targets. These targets are constantly being reviewed based on new events shown in the centralized event database and current business trends. 

An Alteryx workflow derives from this target file our sales expectation for each upcoming day, market, category (full price, clearance) and article type (inline or customized). In order to do so, we use historical data defined by our genes in addition to a set of algorithms and calculate the sales impact ratio of each market and category. These ratios are then used to allocate a target to each one of the combination.

 

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Finally, both pillars are brought together and we derive in a final Alteryx workflow, how many orders and units will have to be placed in each markets and from which article type.

 

However, since certain periods of time have genes combinations that cannot be matched, our working solution also gives us the flexibility to manually override the results. These forecast volumes are then shared with the team, warehouse, customer service call centers, etc. through a Tableau dashboard.

 

Knowledge database.png

 

Describe the benefits you have achieved

Thanks to the work that went into developing this new forecasting model in Alteryx, the adidas WE eCommerce business ended up getting:

 

  • A more accurate forecasting model, which allows for a better planning of our operations.
  • Reduced operational costs.
  • A more detailed forecast as we can now forecast on a daily level, when past methods required much more work and limited us to a weekly forecast.
  • A flexible forecasting model that can easily be modified to include new services and sales channels.
  • A forecast dashboard that lets us easily communicate our forecast to an ever growing number of stakeholders.
  • A centralized event “calendar” that can be used by the entire department for much more than simply understanding the forecast (e.g. it is used to brief in Customer Service teams on upcoming events).
  • A massive amount of free time that can be used to drive other analyses, as it is not required from us anymore to manually join together different marketing calendars and other sources of information, create manual overviews of the upcoming weeks, manually split our weekly sales target, etc.