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