IKEA Ecommerce: A Case Study

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Website ecommerce must ALWAYS be tracked and analyzed in order for any ecommerce company to grow and be successful.  Ecommerce metrics measure both transaction and item data allowing for marketers to observe conversions and create customer profiles from which they can continue marketing efforts (and therefore more profit).  The following post will elaborate on how IKEA used ecommerce analytics to monitor customer traffic. 

Background

IKEA is a global furniture superstore that has positioned itself as the leader in furniture design and innovation made affordable for most individuals.  While their brand is recognized and followed internationally, it’s surprising to think (in this technologically savvy day and age) that most of their sales come from in-store purchases.  Only recently have IKEA officials stressed the need to transition to ecommerce on top of their highly successful brick and mortar stores.  In2013, “IKEA reported an uptick in Web traffic, with 1.3 billion visits were recorded, and the IKEA catalog app was downloaded nearly 10 million times. Visits to physical stores, meanwhile, declined by 1% in 2013 despite the addition of five new stores, including two in China” (Hansegard & Rolander, 2014).  While stores are still thriving, data shows that the demand for IKEA ecommerce is growing rapidly.  Lars Gunnarsson, IT demand manager at IKEA, stated that “‘Ecommerceis our single biggest project. Today our whole business is based on shoppers coming to the store, picking things up and delivering them yourself.  We need to be more sophisticated in service areas.’” (Thomson, 2014)

The challenges IKEA faces are keeping prices low in the midst of home delivery as opposed to in-store pickup and tailoring marketing efforts to each international region they occupy (e.g: North America, Europe, etc.).  The following studies highlight website analytics approaches that have been successful in IKEA’s Australia and United Kingdom (UK) regions.  These efforts could possibly be carried out within different regions to reap the same success within IKEA’s evolving ecommerce model.

IKEA Australia

Any person who has stepped inside of an IKEA store knows that IKEA is famous for their unique in-store layout and customer experience.   IKEA Australia, in conjunction with Match Media, aimed to translate that process within an e-commerce platform.

YOU: Okay, okay, let’s get to the point.  What did IKEA Australia do with their ecommerce and how did they do it?

Step 1: They Gathered Existing Data

While they didn’t explore too far back, IKEA Australia and Match Media took some time pulling data through various resource feeds.  With this data, they found: (Match Media, 2015)

• Revenue and footfall by store, by department, by date
• Website engagement metrics and all online interactions by department, by location and date from Adobe Site-Catalyst
• External conditions e.g. sale conditions, weather, economic climate, key news stories, holiday periods
• To develop the model, they then established a core set of principles:
•  User purchase journeys are neither linear nor consistent - they are made from a diverse range of combined actions 
• The research process and purchase journey length are never immediate - varying significantly by product, department and individual searching
• External factors (e.g. school holidays) impact research habits and business sales

Step 2: They Found their Core KPIs and Business Metrics

The observations above were key into finding what exactly IKEA needed to focus on in measuring their website moving forward.  After heavy analysis of the above, Match Media “used Multi-Linear-Regression (MLR) modeling - taking 70% of the data to ‘train’ the model and 30% to test – enabling it to forecast sales within a 95% accuracy” (Match Media, 2015).  As a result of the formula and further analysis, the following core business metrics were produced for IKEA: (Match Media, 2015)

1.  Number of products added to a ‘Shopping List’
2. Stock Availability checks
3. Visits to local store pages
4. Internal IKEA website searches
5. Number of products viewed

Step 3: They Segmented their Data, Created Customer Profiles, and Targeted their Ads

YOU: Alright, so they measured website data to figure out what exactly they needed to measure…now how can IKEA convert these metrics to actionable data? 

I’m happy you asked.  IKEA Australia ended up partnering with “the Advertiser Database Match (ADBM) product, allowing the company to match Yahoo!7's audience with IKEA customer data sources to target aspiring Australian home decorators.”(Yahoo, Inc., 2013).  The primary goal of this partnership was to understand “if a campaign offer impacted the foot traffic and in-store sales, of consumers who were exposed to the activity online” (Match Media, 2015). 

YOU: Wait a minute….you mean to tell me that IKEA Australia wanted to track their online visitors to see if the visitors purchased an IKEA product in-store?  Integrating online and offline?? 

That is exactly right!  ADMB, Yahoo!7, IKEA matched all of the audience, of whom had been exposed to IKEAs online advertising campaign, “against the 1.2 million IKEA FAMILY loyalty database, then captured the Kitchen transactions of all of those exposed to the advertising and those who hadn’t been and, for the first time ever, was able to attribute kitchen sales uplifts directly to its display activity” (Match Media, 2015). 

As a result, year-on-year post-click tracking performance increased by the following: (Match Media, 2015)

• Business performance increase YOY: 91%
• Cost Per Business Performance decreased YOY: 51%
• Every measurable metric is up by at least 90% YOY and continuing to grow delivering the best business performance on record in Australia.

IKEA Cardiff, UK

From a social media Pay Per Click standpoint, IKEA of Cardiff, UK tested a campaign by matching Facebook usage and EE mobile data to “measure and uplift visits to the IKEA store in Cardiff from those who had seen targetedIKEA adverts on Facebook” between December 2013 and January 2014 (Smith, 2014).  Knocking down two major traffic referrers, social media and mobile, the geo-targeted test campaign “delivered 1.4 million impressions and saw the biggest impact among 22 to 25-year-olds” with “a 31% increase in store visits among this group. The 26 to 35-year-olds were up 11% compared to the non-exposed group” (Smith, 2014).  To be accurate, IKEA made sure to separate staff, people who lived in the Cardiff area, and passers-by from the visitor traffic measured above, but also compared this group against “other mobile numbers that had not been exposed” all while maintaining user privacy (Smith, 2014; Woodman, 2015).  

The combination of PPC data and mobile data, along with careful analysis between the two, allowed IKEA to fully observe how their visitor traffic (both online and off) was being affected and how to influence visitor traffic across multiple channels in the future. 

Which metrics can IKEA use to improve their website analytics efforts?

First of all, it took a good amount of work, time and money to be able to accomplish the data mining feats IKEA accomplished in the above examples.  The metrics they measured were also necessary for them to collect since, at the time, their website and online efforts only allowed for in-store pickup as opposed to home delivery.

IKEAs shift in their ecommerce efforts, from in-store pickup to home delivery, will also (slightly) shift the way they measure their online success.  In addition to measuring number of products added to a ‘Shopping List’, stock availability checks, visits to local store pages, internal IKEA website searches, and number of products viewed within a segmented geographic location, IKEA could benefit from keeping an eye on the following:

1)    Sessions to Purchase

Most people don’t just buy a couch on a whim, especially when they have to put it together.  Measuring Sessions to Purchase will allow marketers “to get a trueunderstanding of how long it takes people to buy” from IKEAs website, at what times in the purchase process are crucial for segmented, targeted advertising, and if that behavior is different across different segments of your website customers (Kaushik, 2006).  Effective analysis of this metric in addition to A/B testing across various paltforms will no doubt optimize IKEAs online conversions.

2)    Visits per Value (total revenue/total visits)

This metric is key in determining where to spend the most amount of time marketing to specific audience segments.  Visits per Value gives you the number of dollars made on a particular visit to your website.   The higher the visit per value,the more valuable the traffic is for your e-commerce business (Sharma, n/d).  You can match specific customer profiles to this data to figure out which customers to target for a specific campaign.  In IKEA’s case, marketers can segment IKEA Family Card holders and figure out exactly how much they should spend marketing toward this group in terms of e-mail promotions, coupons, etc. 

3)    In-Page Exit Rates Within the Shopping Cart funnel

This metric is key.  Measuring these exit rates (i.e. where customers drop-out of the shopping cart/checkout process) will provide information on how IKEA can optimize their cart and what trigger actions IKEA can then take to persuade customers to complete their order in the near future.  Shall they send a trigger email?  Should an advertisement of the items in their cart haunt them through social media ads?  Comparing this metrics next to Sessions to Purchase can further optimize IKEA’s marketing efforts by defining a specific time frame for each advertisement or promotion.

Feel free to share what you have found on IKEA’s ecommerce platform and other metrics that you think IKEA should measure in order to become even more profitable.   

References:

Google. (15 July 2014).  Custom Variables – Web Tracking (ga.js).  Google Developers.  Retrieved from https://developers.google.com/analytics/devguides/collection/gajs/gaTrackingCustomVariables

Hansegard, J., Rolander, N. (28 January 2014).  IKEA Chief Says Focus to Remain on Stores.  Wall Street Journal.  Retrieved from http://www.wsj.com/articles/SB30001424052702303277704579347942836197338

IKEA. (2015).  IKEA 2014: Highlights from Our Year.  IKEA.com.  Retrieved from http://ouryear.ikea.com/story/ecommerce/

Kaushik, A. (21 August 2006).  Excellent Analytics Tip #6: Measure Days & Visits to Purchase.  Occam’s Razor.  Retrieved from http://www.kaushik.net/avinash/excellent-analytics-tip6-measure-days-visits-to-purchase/

Match Media.  (2015).  IKEA: A Data Matching Love Story.  Cream Global.  Retrieved from https://www.creamglobal.com/case-studies/latest/17798/33834/ikea-a-data-matching-love-story/

Sharma, H. (n/d).  E-Commerce Tracking in Google Universal Analytics – Complete Guide.  Optimize Smart.  Retrieved from http://www.optimizesmart.com/e-commerce-tracking-works-google-analytics-ultimate-guide/#ixzz3TCH6vOQF

Smith, C. (14 May 2014).  Ikea’s Facebook Campaign Uses Mobile Data to Prove Effectiveness.  The Guardian.  Retrieved from http://www.theguardian.com/media-network/media-network-blog/2014/may/14/ikea-facebook-mobile-campaign

Thomson, R. (13 November 2014).  Ikea to Roll Out New Web Platform as it Chases 20% Online Sales.  Retail Week.  Retrieved from http://www.retail-week.com/multichannel/online-retail/ikea-to-roll-out-new-web-platform-as-it-chases-20-online-sales/5066258.article

Woodman, I. (2015).  IKEA: Bringing Customers into IKEA Using Facebook.  #IPASocialWorks.  Retrieved from https://www.marketingsociety.com/sites/default/files/IKEA_CASESTUDY2.PDF

Yahoo, Inc. (1 May 2013).  Yahoo!7 partners with IKEA for new product trial.  B & T Weekly.  Sydney, Australia; Reed Business Information Pty Ltd.