Many large retailers, technology companies and the more nimble consumer B2C businesses are harnessing customer data to accelerate organic growth. Most notably Amazon has contributed to disruption in the retail market, essentially blurring the lines between a retail company and a technology company. Its advanced technology and delivery platform has facilitated the success of smaller companies that otherwise could not compete with their larger competitors, and in so doing forever changed the industry by providing universal accesss to e-commerce capabilities. For the rest of the consumer & retail industry this has created an imperative to innovate rapidly in order to secure their survival.
Are there similar signs of imminent disruption within your industry?
How did Amazon accomplish such an incredible feat? As Artemis Berry, VP of shop.org and the National Retail Federation, puts it, “Amazon is a case study in ceaseless innovation and interminable disruption.” Innovations from Amazon include more efficient, cost reducing and free shipment methods; 1-click shopping; Amazon Prime member loyalty program; digital book purchasing and on-demand production; breadth of assortment; unconventional but consumer delighting return policies; dynamic pricing; Amazon devices; targeted automated emailing’s; reviews and ratings; recommendation engines and much more.. Every innovation helps to drive execution of the strategic plan by investing in long-term customer satisfaction/loyalty. Many such advancements were developed based on insights derived from Amazon’s incredible capability in analytics. The supply chain and logistics optimization alone is projected to reduce costs by 10% to 40%.
How is innovation incorporated within your organization’s long term strategic planning?
In addition to Amazon, companies such as Alibaba, Wal-Mart, CVS, Target, The Home Depot, Nike and The North Face have employed analytical methods and algorithms to stay in the game. For example, Nike and The North Face have taken product recommendations to another level, beyond what Amazon has accomplished. These companies are using Artificial Intelligence (AI) to help online customers more easily find and customize products of choice such as digital personal assistants. The Digital Personal Shopper calculates a score for every product in a catalogue and then presents them based on the shopper’s needs, uses previous answers to determine the next most likely question and finally presents high, medium, and low product matches to the consumer. Customers can simply articulate their needs and allow analytics to guide their selection process.
Is your organization developing “code capital” to create competitive advantage?
How to get started incorporating predictive analytics into your organization’s growth strategy
Although the analytical and technological rigor of these companies might seem unattainable for many, all organizations no matter the size can take small steps to implement analytical techniques that drive increased profits. For example, predictive analytics can help companies identify useful patterns in its data by grouping customers into segments, anticipating the behavior of those customers, and generating business insights that support or even suggest strategic actions. Companies can also use its own data to identify and quantify the drivers of customer purchasing behavior, as well as their levels of satisfaction and likelihood of attrition. Furthermore, deployment of these tools will be vital in establishing an effective CRM strategy to support sustainable organic growth. The outputs of the these models will allow individual companies to tailor and present more personalized product recommendations to customers throughout their life cycles, thus increasing average spend, growing lifetime value, improving retention, and strengthening brand loyalty.
When it comes to incorporating analytics into business management and decision support, one of the biggest barriers for leaders is knowing how to structure the work required to extract the necessary insights and value from data. Without a structured approach, organizations often lose sight of the end goal and allow lack of priorities to become obstacles to achieving success. Every organization will have to deal with some combination of these challenges, including lack of access to information, poorly structured datasets, static and outdated analyses, and a shortage of advanced analytical capabilities. Keep in mind that the end goal is not to have perfectly clean data sets or to have an in-house team of top data scientists. Rather the goal is to leverage data you already have to uncover actionable insights that drive profitable growth and to institutionalize a data driven foundation for decision-making. Much of the technical work along the way can be contracted to outside and specialized resources as long as the desired inputs and outputs are well understood, which is where the secret to success lies.
Viewing your organization and customers through these lenses often leads to changes in strategic direction and a new sales trajectory. It can help to build a deeper understanding of your current performance challenges, uncover new pockets of growth, and expose sources of competitive advantage. Can you afford to stand idle and wait to see what’s going to happen next? I think not.
A Structured Approach to Help Get Things Started
Customer Segmentation Analysis
Certain analytical techniques (e.g., clustering) work better in identifying complex patterns in consumer data . Prior to model input, the data will need to be pre-processed to fit the model of choice. Generally, multiple modeling approaches are used and may even be combined into an ensemble model; the results are then evaluated for performance and the best model is selected.
Results of a clustering model for example may produce more meaningful segments, and possibly sub-segments, with homogenous characteristics of consumers if the data is properly selected and prepared. These homogeneous consumer characteristics could include demographic, geographic, psychographic, behavioral, cultural, generational similarities. In addition to defining homogenous characteristics related to the needs and preferences of each segment, business metrics such as total sales, profitability, return rates, and customer defection can be associated with the consumer groups.
In line with characterizing the consumer segments is understanding their behavior. Part of this analysis involves churn modeling with the segmentation groups as an input variable; it’s purpose is to identify customers at increased risk of voluntary churn and identify early churn signals. Churn prediction is useful in the consumer sector since a good rule of thumb is that acquiring a new customer costs 5 times higher than the cost of retaining a current customer.
The churn models that are built in the analysis are evaluated against standardized performance metrics to determine their usefulness. It is important to evaluate the models’ performance for customers and accounts prior to their departure so the company can deploy retention efforts prior to defection.
Understanding which products each customer segment purchased, which products are frequently purchased together, and in which sequence the products are bought is essential.
Various modeling techniques can provide visibility to cross-sell and up-sell efforts. A seasonality analysis is also important to determine at what times products were purchased most frequently in each segment. Insights from this analysis can provide the company with customer-centric recommendations that will enable the business to create marketing strategies tailored to the different segments and times of the year.
Ready to discuss building predictive analytics capabilities within your organization? We’d love to hear from you.