Adidas: Creating a Customer-Focused Experience Through Data

Adidas: The Importance of Consumer Data

Long gone are the days when marketers could analyze their ecommerce site solely by purchase per SKU/style alone. Now, there are numerous tools that can be used to not only see how what customers are purchasing, but how they are purchasing them, what led them to the purchase, and much more. There are no more guessing games. Retailers have a great chance of understanding their customers if they are focusing on specific points of data that will help drive their business goals.

Adidas is one brand that places a large emphasis on data-driven growth. In fact, Adidas’ Global Head of Digital Brand Commerce stated,

“Digital is one of our core strategic priorities…We will measure ourselves rigorously in how the various digital touchpoints come to life across those three dimensions, and we believe they will be core to reaching our overarching targets for sales, consumer experience net promoter score, and ultimately engagement and lifetime value” (How Consumer Insights and Digital Have Led to…, 2018).

When consumer behaviors and even technologies change, Adidas is on board to change right with them. Adidas is also concerned with creating meaningful stories in real time so they can gauge customer reaction instantaneously. It’s all about the consumer experience, and the Adidas app is a great example of carrying out that goal.

At the core of this consumer experience is Adidas’ shopping app. Before their ecommerce app was created or launched, the Adidas team tested it with a specific set of consumers and launched it before all features were developed. That led the consumers to provide feedback, which fueled what Adidas did next and which new app features should be prioritized (How Consumer Insights and Digital Have Led to…, 2018). This feedback shaped the consumer app experience in a way that was asked for directly by the users themselves.

Adidas even employed data scientists to create a Consumer DNA (CDNA) model that creates a 360-degree view of consumer behavior.

“Although sales is still an important KPI, [Adidas] also measures influence as a new currency in a digital world to create brand awareness, interest and desire with consumers. CDNA data points continuously expand to include a wide variety of external sources of information. Data science algorithms called genes then iterate through billions of records every single day continuously updating and adjusting profiles based on the latest available consumer data” (Underwood, 2017).

The idea is for customers to build their profiles through the Adidas site or app, so that Adidas has a better understanding of each specific customer. The customer focus is proving to be successful for Adidas, as they are able to test new ideas and campaigns through A/B testing, which allows for better understanding what works and how to tweak a campaign where applicable before it is fully completed.

So how does this work with consumer experience align with SEO tactics? On-page SEO tactics are used primarily through heading keywords related to Adidas collection names. Instead of including generic terms like “running shoe,” for example, Adidas lists the exact collection name in their website headings. Including these specific keywords is a bit risky, but to compensate in regard to search, the description keywords include both the generic term as well as the collection name, as you can see in the “track pant” search example below:

Additionally, Adidas’ Site Map is a great tool for those browsing the site. Below is a screenshot of Adidas’ Site Map, which was updated about a month prior to the date the screenshot was taken:

(Where can I find a site map?, 2018).

According to Michael H. Fleischner, site maps serve two purposes. “First, they make it easy for visitors to find content on your site, and second, they enable search engines to spider your site much quicker…I strongly believe they are helpful in the optimization game…” (Fleischner, 2016). Users get a quick glimpse of the site’s products. This is especially useful tool for a brand like Adidas, which offers products in numerous buying categories.

Adidas Web Site: My Ideas for Web Analytics Efforts

With the customer experience as a central focus, Adidas must be sure to emphasize analyzing demographics, social sharing and influencer activity, device tracking and browsing patterns in their web analytics efforts. Below, I will go into further detail for each metric:

·      Demographics and Location

Adidas is a global corporation. They must use SEO and web analytics to help determine whether their sales targets per country are on track and also what types of customers are purchasing specific products and categories. These insights can help the Adidas marketing team understand whether they are targeting their campaigns in the correct manner or if they are missing the mark.  

·      Social sharing and influencer activity

Adidas is well-known for using prominent celebrities and public figures to promote their products. For example, actress Shay Mitchell appears on their main page promoting Adidas’ Statement Collection. Adidas will benefit from tracking whether this link helped drive traffic to the Statement Collection product page, or if any other campaigns to promote the Statement Collection worked better (or worse). Any social media campaigns featuring celebrities and influencers must be analyzed to see where the ROI figures lie.

Additionally. Adidas could improve their SEO by display social links on their site. These links do not even appear on the US site main page! Prominently displaying these links would help give Adidas even more precious data on how customers are using social platforms and how they are reaching them, which can be tracked through referrals.

·      Device tracking

The ecommerce app was Adidas’ idea for a bolstered customer experience. Therefore, to analyze the fruits of their labor, first all devices must be adjusted for SEO purposes so user searches look attractive, regardless of the device used. Second, device analysis must be done to determine where users are browsing the most, which includes analyzing metrics such as time of day and bounce rates. Purchase conversion rates must also be analyzed per device, and could be an additional fail-safe in case one type of device isn’t optimized effectively for purchasing.

·      Purchase patterns and purchase conversions

Analyzing user flow reports and funnel reports, such as those offered through Google Analytics, can help marketers at Adidas understand how users are browsing their site and also measure things like cart abandonment rates.

“E-commerce platforms are even designed to help you keep up on cart abandonment with built in autoresponders to help win back abandoned carts. Pixels are even in place for many brands to setup ad retargeting for customers that bail on the checkout process. But are you looking at the rest of your funnel to see where customers are dropping out during the shopping experience?” (Patel, 2018).

Using tools like Google Analytics, marketers can also determine things like purchase conversions, and any other type of conversions as deemed important enough to be a business goal. Adidas might want to consider goals related to returning customers and purchasers, average purchase value and purchases made through organic search alone.

What other ways can Adidas utilize SEO to effectively bring home their goal of creating an individualized customer experience?

References

Fleischner, M. H. (2016). SEO made simple: Insider secrets for driving more traffic to your website. United States: Michael Fleischner.

How Consumer Insights and Digital Have Led to Adidas' Growth - SPONSOR CONTENT FROM GOOGLE. (2018, May 07). Retrieved September 20, 2018, from https://hbr.org/sponsored/2018/05/how-consumer-insights-and-digital-have-led-to-adidas-growth

Patel, N. (2018, June 25). The Metrics Every E-Commerce Store Should be Tracking. Retrieved November 18, 2018, from https://neilpatel.com/blog/e-commerce-store-should-be-tracking/

Underwood, J. (2017, June 14). How adidas Transformed Analytics Platforms for Digital Scale. Retrieved November 17, 2018, from https://www.jenunderwood.com/2017/06/14/adidas-analytics-platforms-digital-scale/

Where can I find a site map? (2018, October 15). Retrieved November 18, 2018, from https://www.adidas.com/us/where-can-i-find-a-site-map.html

Google Analytics vs. Clicky Analytics

Google Analytics- What Is It Good For?

It is no surprise that Google Analytics (GA) receives a lot of buzz as one of the most popular web analytics tools. In fact, GA is often considered the industry standard, and the fact that it is a free tool is another large draw for users. To put user base into perspective, close to 75% of the top 100,000 websites use GA, as of a publication from July 2018 (Ringvee, 2018). Besides the fact that GA is offered as a free tool, there are multiple other reasons why marketers and businesses choose it for their web analytics measuring tool:

1.     Automatic collection of data

2.     You can create customization reports

3.     Easy integration with other tools and platforms

4.     Ability to measure internal site search

5.     To understand why visitors are bouncing off your website

6.     To know the age, gender, interest, device and location of your audience

7.     To understand which social platforms to target

8.     To understand what kind of content you should write

9.     To check if you are achieving goals (Thakur, 2017).

…But There Are Also Some Cons to the Google Analytics Platform

 Heavy users of GA tend to have a love/hate relationship with the tool, the “hate” portion coming primarily from the number of changes that Google roles out on a regular basis. To be fair, “hate” is likely too strong a term in this scenario, and the word “frustrating” would be better suited. GA users are forced to adjust to the changes, but change generally brings about some level of frustration for most.

In addition to these changes, GA has some terminology and “tainted” data that marketers must understand to fully utilize the platform. Analytics blogger Neil Patel labels these as GA “lies.” These “lies” include the following:

1.     “Dark traffic” on the rise- This can be thought of as hidden traffic. GA lists this as “direct traffic,” as it is too difficult to track every type of traffic source. As an example, Groupon discovered that as much as 60% of their direct traffic was actually organic traffic:

(Patel, 2018).

The solution? Utilizing UTM builders (which enable you to add code snippets to URL’s) or being sure to tag your links across social and email before they go live.

1.     Hidden social referral traffic- Since Google and Facebook, for example, track visitors in different ways, the data is not always accurate. This is an opportunity to use the Google URL builder to make sense of the data.

2.     Emphasis on vanity metrics- One primary example is visitors. The marketer/user must realize that a large monthly visitor rate is not necessarily a positive thing. Metrics such as exit rates and bounce rates must be analyzed as well.

3.     Misleading ghost spam links- What is ghost spam? Good question. Here’s a good infographic explaining the process:

(Patel, 2018).

            To help curb this issue, Bot Filtering can be turned on in GA.

4.     Positive A/B tests- False positives are common in these tests, and because 5 variations (instead of 2) must be tested for the best results, these tests take a lot of time for the tester. “If you let more people through the gate to your site, but they are lower-quality leads, then positive A/B tests won’t always tell the truth.”

5.     Too much emphasis on leads instead of sales- Closed sales are worth more than leads, and lifetime value (LTV) of a customer is even more important. Marketing analytics and sales teams must merge to create specific goals relating to sales over leads alone.

6.     False conversion costs- As an example, if a customer sees your ad online, they might not click on it right away. However, if they remember your brand and the specific offering, they might conduct a Google search at a later time based on the ad they saw prior. This will not get tracked as direct traffic, but it can be seen as direct traffic nonetheless. In order to alleviate this issue, you should consider factoring in the timing of a specific campaign into your conversion rates. This can help you understand whether the campaign led to the increase in sales.

7.     Last-touch attribution bias- “Last-touch attribution is when Google gives all the credit of any given conversion to the last touch point before the conversion” (Patel, 2018). In relation to sales, GA takes last-touch attribution into account, which is why multi-touch attribution must be analyzed when looking at conversion rates. Here’s a visual for last-touch attribution:

(Patel, 2018).

These “lies” are important to understand in order to make sure you are working around them to obtain the most accurate data possible. These “lies” do roll over to other web analytics platforms as well; GA is not the only

All of this being said, there are some other web analytics platforms that rival GA, even with taking the good and not-as-good aspects of GA into consideration. In this post, I’ll be discussing Clicky Analytics in particular, a well-reviewed web analytics platform.

 

Clicky Analytics- How It Compares to Google Analytics

To start, below are some features Clicky Analytics offers that GA does not:

1.     On-site heat maps- An example can be found below. These heat maps can also be segmented via split tests, which help Clicky users understand how visitors are clicking on various versions of landing pages. 

(Taplin, n.d.).

2.     Your favorite stats can be accessed through one button- Clicky users can post a short code on their site, giving them an icon to their favorite stats that only they can access by logging in and using their specific User ID.

3.     Real-time information about all of your visitors- This is self-explanatory. The difference here is that GA has about a 24-hour delay in most cases.

4.     A simple interface displaying the day’s stats- Compared to GA, the user interface is considered easier to use. Here’s a quick look at a typical dashboard:

(Taplin, n.d.).

5.     A better way of measuring bounce rate- Here’s the breakdown directly from the Clicky blog:

“A visitor who has only one pageview, and who is on your site for less than 30 seconds is what we now consider a bounce. So any visitor who has more than one pageview, or any visitor who has only one pageview but is on your site for at least 30 seconds, is now what we consider "engaged" / not a bounce.

Say you have a blog post that you have shared on a few social media networks like Facebook and Reddit, and you get 1000 visitors to it. Chances are that 95% of these visitors will only view the article that is being linked to - one pageview. Maybe half will read the whole article, half will read part of it, and a few will click through to your front page to see more. Any other analytics service would report a 95% bounce rate for these visitors. But a bounce is negative, so it makes it sound like only 5% of these visitors were engaged. But that's not true at all - half of them read the entire stinking article!” (Why Clicky’s new bounce rate is the best…, 2010).

6.     Video and Twitter analytics- Clicky measures things like when users hit pause and how long they watch a specific video, called “pause-seek monitoring”.

7.     Search rankings position for each visitor- This one is pretty self-explanatory as well. Here’s a look at what a ranking statistic would look like on Clicky:

(Taplin, n.d.).

8.     Live links- Again, this real-time offering is something that GA doesn’t offer.

In addition, Clicky is offered for free, but the Pro+ and Platinum packages, at a monthly cost, offer additional tools for subscribers. According to writers at Blogtyrant.com, Clicky Analytics is a great option for serious marketers serious about using web analytics to grow and expand their business.

Please leave a comment if you have any experience with Clicky Analytics! Let us know what you like and what you don’t like as much as compared to Google Analytics.

References

Patel, N. (2018, June 27). 8 Data Lies Google Analytics is Telling You (And How to Fix Each One). Retrieved November 11, 2018, from https://neilpatel.com/blog/google-analytics-lies/

Ringvee, S. (2018, July 30). Most common Google Analytics issues (in-depth overview). Retrieved November 11, 2018, from https://reflectivedata.com/common-google-analytics-issues-in-depth-overview/

Taplin, R. (n.d.). A Review of Clicky Analytics (and Why it Beats Google). Retrieved November 11, 2018, from https://www.blogtyrant.com/a-review-of-clicky-analytics-and-why-it-beats-google/

Thakur, D. (2017, July 20). 10 Good Reasons Why You Should Use Google Analytics. Retrieved November 11, 2018, from https://medium.com/@dineshsem/10-good-reasons-why-you-should-use-google-analytics-699f10194834

Why Clicky's new bounce rate is the best in the biz. (2010, April 26). Retrieved November 11, 2018, from https://clicky.com/blog/214/why-clickys-new-bounce-rate-is-the-best-in-the-biz