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Driving Ecommerce Growth Using Big Data

Driving Ecommerce Growth Using Big Data – How to Get Started

Making the most of the information and systems you have today in creating an engaging UX to the customer.


Over the last two years, there has been explosion of conversation on growing your ecommerce business using Big Data. While using previously untapped information is great as a message, where is the roadmap for applying this concept in real life?

Most IT leaders are missing a critical step in making the move to using this powerful new data.


Key Challenges

  • Most organizations don’t have the right team in place to exploit and use data past the day to day product and transaction types
  • Convincing an already successful business to invest in a data mining and analytics team can be a very tough mountain to climb
  • Knowing what capabilities your existing ecommerce platform has
  • Nearly every company that hasn’t yet begun to leverage advanced analytics and external data is stuck in a loop of not knowing where to start and thus experiences a failure to launch


  • Understanding your challenges will lead to growth
  • Fully evaluate where you are today
  • Ask the question, do we have the team in place that can exploit this data?
  • Don’t build Rome in a day. Explore the easiest implementation methods at hand
  • After implementing the low hanging fruit, its time to become more advanced


Here is one of the most common scenarios in an ecommerce landscape today: you are responsible for cultivating, curating, growing and ultimately driving revenue growth and conversion for your company’s ecommerce business. The biggest problem you have though is feeling like you’ve done everything you can to grow the business and now feel powerless as the numbers just aren’t where they need to be. On top of this problem, the subject of a support budget (or the lack thereof) comes up at least once a quarter. The source of contention seems to always be a lack of appreciation for the growth levers past the usual concepts. As of late, the ecommerce industry seems to be talking nonstop about Big Data, Data Warehouses and Analytics. That’s great for the few that seem to have it all figured out, but what about my company? We have tons of data, lots of customers and even more untapped customers that need to be connected to our products. How can we make the leap to begin using all of the information that seems to be just outside our grasp to grow our business.
In today’s world of an over abundance of data, it can be overwhelming as the person who is responsible for an e-commerce business to understand and know how to use data that is available to you in the most effective way possible.
The barrier of using the data available, in its simplicity, seems to be the complexity of the information itself. Its like having to climb a mountain of data, just to understand what it is that you have and then the problem of how to use it arises. In my experience, this data can be some of the most useful and game changing information that is available to you and if tapped in the right way, can change the game, improve sales, conversion and ultimately the overall user experience tremendously. Basically, let’s stop talking about Big Data and let’s actually begin the process of knowing what it is and how to use it!



One of the biggest challenges that many companies have in understanding how to begin using data and analytics is having the right team in place to exploit that data. Having the knowledge and skill set on your team is the fundamental origin to building the next phase of your e-commerce landscape. Not only should you have data architects and data analyst but also having strategist and UX team members will certainly help jumpstart this process. Even if the budget doesn’t allow for all of these team members to be added immediately, adding a data architect and a senior UX strategist will launch you into the realm of understanding what data you have and how you might be able to use it more effectively. Consultants are always an option for this process however it is my experience that growing this knowledge internally is the best and the most long-lasting solution. Fully evaluating where you are today is half the battle in getting off the launch pad.
Another key challenge that I have experienced over and over again is convincing an already successful business that a Data/UX/Analytics team is a smart investment to grow the ecommerce business. Typically, this is a company that has a strong distribution business and is now looking to grow their e-commerce business at the same time. Again, typically the distribution business is doing well but the e-commerce business has its own growth goals and is held to the same standard of growth that the distribution arm experienced. The best and most convincing argument for growing the e-commerce business through data is illustrating the kinds of data that have been previously untapped and showing how the customer experience could be shaped, molded and grown in positive ways. I will talk about this more in my recommendations below.

One of the biggest challenges that every e-commerce stakeholder has is knowing what capabilities your existing e-commerce platform has in the way of improving your customer’s experience through the leveraging of data. Many times this information is known by your system architects but because the question has never been asked, the subject has never come up of leveraging the marketing, product promotion and customer segmentation capabilities. For instance, IBM’s WebSphere Commerce platform has an entire precision marketing based system. It is extremely robust with hundreds of out of the box variations to better market to the customer. If you do not know this exist, it would go completely untapped. By simply opening this ability, your system would instantly become more relevant and inherently useful to the customer.

Here is a quick list of examples that WebSphere Commerce precision based marketing can offer:

  • Marketing activities that support trigger, target and action elements which creates a highly engaging experience
  • Marketing statistics that provide additional data about marketing activities for the marketing team to continue improving its campaigns
  • Automated messaging and dynamic customer segmentation
  • Automated experiences based on the existing customer data and their corresponding customer segmentation
  • Marketing migration utility which enables the management of existing activities
  • Mobile marketing which enables the creation of marketing activities that send text (SMS) messages to customers
  • Targeting capabilities that support behavioral targeting from external sources (i.e. search engines)

Magento is another common platform that natively supports precision based marketing leveraging the Magento library of extensions. This is quick list showing some of the capabilities:

  • Create rules
    • Based on Customer data
    • Based on Orders (orders quantity, sales amount, purchased quantity)
    • Based on Shopping cart (grand total, number of different products, total items quantity, subtotal)
    • Based on Products (product list, product history)
  • Design custom customer segmentation
  • Create types of advertising campaigns – target all customers and visitors.
  • Create user package types – distribute advertising to the customers whose account or ID in the list of user package, user package is the list of emails or IDs.
  • Easily export segments to CSV/XML for easy use by the marketing team
  • Dynamically create re-indexing rules

All platforms having some level of out of the box marketing using a deeper level of data. Uncovering what your platform can do is the first step in the journey.

The central thought to all of the key challenges mentioned here is that every company that has thought about leveraging advanced analytics and big data has not started because they are stuck in the loop of not knowing where to start and thus experiences a failure to launch. IT leaders in the Ecommerce space wanting to go into this area must first put together a package showing the benefits of quick growth that advanced analytics can offer.  Showing what your system can do today, will only help grease the wheels and get things moving to ensure backing in the future.


After better understanding what capabilities your e-commerce platform has natively, the next step is to evaluate where you are today in terms of the data that is available to you from your other systems. Perhaps it is an ERP system, a shipping system or even an email marketing system that has pertinent data to your customers however is currently unconnected to the primary database. Tying these bits of information together is where the picture will start to become much clearer and the message more relevant. One of the best ways to build growth is to build loyalty. To build loyalty, you need to speak in a relevant voice to the customer at all times. Being relevant means that you understand them and that requires all the data about your customers. At this point, we are all familiar with Amazon.com and its ability to always speak to you in a relevant voice. If you have any order history with them at all, you will see this relevancy once you login to the homepage. A wealth of information about other products related to your buying behavior immediately is presented. This bypasses any hesitancy that you may have and immediately engages you in buying decisions based off of previous decisions you have already made. Being relevant requires that you understand where your data resides today and being able to pull that information together into one place so it is useful to the overall UX.


Once you have your data identified and a high-level strategy put in place, it is time to think about the team that will take this data and make it meaningful to the customer. The two key members of this team will be your lead data architect and your lead UX architect and strategist. These two members will then lead their teams in building and connecting the dots in utilizing this newfound data. It is important to remember that this is a large undertaking and no single person can do all this work. This is certainly a team concept that will take a multifaceted approach in building a solution.


As I mentioned, one of the biggest reasons that any company fails to launch a more in-depth approach in relating to its customers is that it is a large concept. As we have discussed, exploring the easiest implementations is certainly the best place to start. Using what is out of the box is a great place to start and incorporating any other pertinent data that can be pulled from other systems will slowly build this clear picture to show marked improvement. Never wait to have the “full picture” or every bit of data that you can find before implementing. You will never have a perfectly clear picture as your customers will always be changing and creating new ways for you to be relevant to them. As we have discussed, funding for these ventures is important and showing progress initially is critically important in getting top down support as you build this part of your business. When it comes right down to it, don’t bite off more than you can chew right off the bat.

There are two ways to get things started initially in terms of “easy to implement” strategies. The first is to setup dynamically created customer segmentation. Knowing exactly who your customers are in terms of classifications will help break down who exactly you are engaging with. When using Google analytics, there are a couple of shared customer segmentation reports from the report library that are tremendously helpful right out of the gate. Implementing these will help you understand who is coming to your site and if are you measuring up to what you actually want. My current favorite is called Occam’s Razor and has not only customer segmentation but also many other excellent tools. The second low hanging fruit is marketing experimentation. As I mentioned earlier, IBM WebSphere Commerce allows you to do experimentation with your workflow paths and your marketing opportunities site wide. Experimentation could be as easy as an A/B testing between two different ads to see which converts better or something as complex as understanding where a customer came from and then placing them in a specialized path that is designed to convert that customer.

The most important thing is just to begin trying some things and you might just surprise yourself in the marked improvement you see in your conversion rates.


Once you have gotten the hang of simple and easy methods of involving relevant information and experiences for your customer, it is time to become more advanced in your approach. Typically, the more advanced approach uses data mining, forecasting and statistical analysis to get the job done. The most important concept in these aspects is ensuring that the customer feels like you know them and you are speaking to them in a relevant voice. Becoming advanced for the sake of being advanced has very little return. Using advanced techniques to assure the customer that you know them will create loyalty and therefore a higher return.

Here is a list of more advanced techniques to get you thinking:

  • Data Mining
  • Predictive modeling life cycle
  • Improving Data Governance (as your database expands)
  • Being able to create, validate and monitor your data and analytical models
  • Building and Scoring credit risk models

Statistical Analysis

  • Being able to analyze data in a flexible and scalable environment
  • Ability to grow your data to immense size without the loss of data accessibility
  • Engaging the entire enterprise in feeding data
  • Being able to visualize the data graphically through a visual interface


  • Ability to model and simulate business over a period of time with outside influences
  • Combining the predictive modeling abilities to engage the customer with precision

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