According to the CEO of one of the leading e-commerce floral companies, the business was successfully growing by 10% every year from 2002 to 2011. But with the introduction of in-house analytics team, the business growth has boosted to 30%.
Analytics has helped many start-ups in many possible ways. Analytics allowed them to predict their capability of how customer demands could be met, by understanding the traffic patterns and average delivery time for each supplier in major cities. This allows them to make and meet the commitments, or pass on the business where they know that delivery is impossible, or to suggest a next day delivery. Analyzing supplier performance helps them identify which of their suppliers will fulfil the given order based on their location. The customer complaints are analyzed to drop poor performing suppliers due to on-time or product quality issues. Also one can identify those customers, who are more interested and are more likely to come back and repeat business so it helps in optimizing your marketing investment. Building long-term relationships with customers maximizes the value. Turning the analytical focus inward has helped significantly to improve staffing levels forecasts which help to manage operational costs. Even the positioning of products on the website is measured using analytics to identify the best location to help drive engagement and sales. In order to understand which are the most popular products or a combination of products and this can vary regionally as well as seasonally, one needs active support of the analytical tools used for the business purpose. Analytics also help them to target the right product at the right time increasing sales and customer satisfaction.
Now to throw some light on how start-ups should be doing analytics- an overview has been given stage by stage for better understanding.
The most important thing that company needs to make sure that they are measuring their product properly, because it is the product metrics that will help them iterate quickly in this critical phase. The one in an ecommerce business really needs to make sure that his Google Analytics (GA-web analytics tool by Google) ecommerce data is good. GA can track ecommerce business all the way from visitor to purchase, so it is a necessary requirement for the company. Any tool such as Mixpanel (Product and User Analytics by Mixpanel) and Heap (Mobile and Web Analytics) all are equally good. Any financial reporting should be done in Quickbooks (accounting software package developed by Intuit) and forecasting should be done in Excel. Baremetrics (Subscription Analytics developed by Crunchbase) for subscription metrics can be used if one is interested to invest in a subscription business. If someone is in an ecommerce business, use shopping cart platform to measure gross merchandise value (GMV).
Now to help start-ups grow at faster rate more reliable metrics, more flexibility and a better analytical platform for future growth is needed. For that one needs to set up data infrastructure. This means choosing a data warehouse, an ETL (extract, transform, load) tool, and a BI tool. For data warehouses, use Snowflake and Redshift . For ETL tools, use Stitch and Fivetran. For BI use Mode and Looker.
This stage is potentially the most challenging. In this stage start-ups have a relatively small team and few resources, and their work can directly impact the success or failure of the company as a whole. To ensure success, start-ups should implement a solid process for SQL-based data modelling. The data models serve as the underlying business logic for analytics.
This stage is all about creating analytics processes. Here start-ups can start implementing data testing. Use pull requests and code reviews. Any analytic code is an asset, just like the code that powers your website and application. Get every one of the team members in git, train them how to use branches.
Though traditional business intelligence (and data mining) software does a very good job but it has some disadvantages which can be overcome by predictive analytics. Predictive analytics uses data patterns to make forward-looking predictions that guides start-ups to as where they should go next. Predictive analytics help them in targeted direct marketing. By integrating customer data from multiple web and social media, these companies can determine promotional effectiveness from narrowly defined customer segments, from location, or from delivery channel. By using proper predictive analysis, they can display the best ad based on the likelihood of a click, as well as the bounty paid by its sponsor. Analytics also help in fraud detection. Businesses need to minimize false insurance claims, inaccurate credit applications and false identities and analytical tools help them doing so. Also predictive analysis helps in investment risk management .One can do “big data” analysis to find out whether one is contemplating an investment in his favorite start-up, or a little-known stock on a public exchange because the decisions can’t possibly be evaluated by individuals without predictive analytics. Customer retention is another field where also one needs predictive analytics. Every business should have the necessary model to predict which customers are about to leave, and for what reasons, so they can target their retention efforts. A retention campaign may cost more than it gains if predictive campaign is not conducted.
There are few more examples where predictive analytics are doing wonder such as movie recommendations. Movies are selected, or recommended to customers, based on past reviews, related interests, or analysis of Twitter comments. Next is providing guidance for education studying and targeted learning. Every quiz show expert would like some guidance to identify which question areas need more study and every student needs help on how to spend his limited study hours more effectively. Schools need the same analysis to provide more effective teaching media and techniques. So start-ups which are dealing in education sector are greatly benefitted by analytics. Another example is clinical decision support systems. With costs escalating in healthcare today, it’s more important than ever to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses. Additionally, predictive analytics can help make the best medical decision at the point of care. So start-ups attached with this type of facilities will be greatly benefitted. Also insurance and mortgage underwriting are using analytics on a large scale. Predictive analytics will accurately determine a reasonable premium for auto insurance companies to allow them to cover each automobile and driver. Similarly a financial entity needs to accurately scrutinize a borrower’s capacity to pay before providing a mortgage to minimize credit/default risk.
Some experts termed predictive analytics as “business analytics” intending to define a holistic term including data warehousing, business intelligence, enterprise information management, analytic applications, and enterprise performance management. This business intelligence market is growing nine percent per year, with about 50 percent contribution from predictive analytics. So analytics can revolutionize present era specifically boosting the operations in start-up.
By Samoshri Mitra