25 Jul ASK THE EXPERT: What every e-commerce entrepreneur needs to know about advanced analytics
Starting a business is exciting, but it often comes with many risks and uncertainties. For most entrepreneurs, having a trusted resource when problems arise can often be the very element needed to achieve game changing success.
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Cara Monroe is a Dallas based technology expert at Point B, a management consulting firm that assists clients in developing strategic insights and translating them into impact. She has 10 years of experience in Information Technology and has worked with many Fortune 500 clients to develop IT strategy, support complex software implementations, lead change management initiatives and support organizational transformation.
Prepping for Advanced Analytics on a Startup Budget
By Cara Monroe
So you’ve started an e-commerce business? Congratulations. Chances are you already know how important data will be to your business, but may not have the millions of dollars you’ll need right away to leverage the power of Advanced Analytics. For the cash-strapped entrepreneur there are three guiding principles to focus on in the short term that can provide a solid foundation in order to reap the benefits of advanced analytics in the future.
What Is Advanced Analytics Anyway?
Advanced Analytics is an umbrella term used to describe a broad range of analytics that are intended to give businesses greater insight into their data in order to ultimately drive better business decisions. Popular techniques that can be leveraged in this space include machine learning, predictive analytics, and location intelligence each of which promote the use of both structured and unstructured data. Advanced Analytics is intended to enhance an existing analytics infrastructure by providing you with a 360-degree view of data from Data Marts, Data Warehouses, best-of-breed, legacy systems, and other data sources.
Data is of particular importance for any business that is seeking to gain market share and differentiate themselves from its’ competitors, but in order to do so, they must build an integrated technical architecture from the beginning. For e-commerce businesses, analytics can be critical in driving and improving conversion rates. In fact, a built-in analytics system is essential for every e-commerce website. If you can’t collect relevant data, you can’t learn about customer behavior, which is at the heart of all conversions. Simply put, a focus on analytics is important to run an e-commerce business successfully. Additionally, creating a solid technical foundation from the beginning will allow your e-commerce business to scale effectively and leverage the power of more advanced analytics techniques. Here are three tips to consider when getting started:
Build A Connected Ecosystem
For an e-commerce company, building the right technology stack from the start can be a complex endeavor. You may find that customer and order data is spread out across your website, multiple sales channels, inventory managers, marketing platforms, fulfillment providers, and financial reporting tools. In order to benefit from the most innovative techniques in analytics you need to start thinking about how you will build a scalable and flexible architecture that can grow with your organization as you are able to enhance your capabilities. One way to do so in the near term is to leverage the power of APIs for real-time data exchange between systems. Doing so can help to automate fulfillment workflows, maintain a single source of inventory between channels and allow for easier data extraction and normalization.
As your business grows and your technical stack expands to include a Customer Relationship Management system (CRM) or and Enterprise Resource Planning tool (ERP),you may consider adding middleware once your servers and infrastructure can no longer manage large amounts of data and transactions due to a wide range of expanding and diverse operating systems, applications, and environments. Middleware is a translating middle layer that can make these systems, applications, and environments communicate seamlessly at a time. In the most mature future state, many businesses adopt a multi-tier client/server architecture and infrastructure supported by several middleware technologies.
Use What You Have For Now
You will most likely want to use the primary sales channel as the primary data source in the early stages. Fortunately, most of the most popular platforms such as Shopify, Magento and Big Commerce offer built-in business intelligence solutions for instant reporting and dashboards with very little customization required. Using these tools will provide access to critical key performance indicators (KPIs) such as daily sales, conversion rate, site traffic and shopping cart abandonment rate. For more complex metrics, such as Cost of Goods Sold (CoGS) integration with an additional tool may be needed.
Aligning organizational goals to performance indicators and monitoring these metrics daily to drive business decisions and actions is a required behavior. Analyzing business outcomes with these metrics in the initial stages will help the organization understand where deeper insights are required. These deeper insights should inform how the infrastructure for advanced analytics should be prioritized, designed and delivered in the future.
Buy What You Can Afford As You Can Afford It
Technology is almost always a significant investment. As a result, every organization will regularly need to prioritize new implementations according to the expected return on investment. Depending on the organization’s goals, implementation of a CRM, ERP or Enterprise Data Warehouse (EDW) will require proper sequencing and planning.
For an organization looking to expand its analytics architecture implementing an EDW should be the first step. This data warehouse should act as the primary repository to store and manage structured data. To keep costs down, using bundled in business intelligence (BI) tools, integrated ETL software, and leveraging virtualization tools should be considered keep infrastructure costs manageable.
Big data analytics platforms, such as Hadoop, are actually built to compliment the traditional will most likely be a traditional relational database management systems (RDBMS) like most data warehouses and can be layered into the technical architecture as the need for more sophisticated data analysis emerges. In the most mature future state, an organization will use an EDW in collaboration with more advanced tool for data management, data mining, and the monetization of large amounts of unstructured data for a competitive advantage.