Advanced Customer Analytics in Customer Acquisition and Retention

Advanced Customer Analytics in Customer Acquisition and Retention

COVID-19 has influenced a variety of lifestyle modifications, including changes in consumer purchasing behaviour. Customers now engage with brands on their terms and demand a personalised and meaningful experience, as opposed to feeling “forced” to shop online. The Indian e-commerce market, which had 150 million online shoppers as of FY21 and was the third-largest market overall, is predicted to reach US$ 200 billion by 2026.

From Retail Industry’s Perspective

Forrester forecasted that for businesses to succeed in 2020, they would need to use consumer insights and quantify the economic effects of CX projects. Gaining and keeping customers has been more expensive as traditional shops switch to e-commerce. Therefore, following, interacting with, and comprehending the consumer throughout their trips is crucial.

Holistic Customer View Point

According to research, using customer-related variables and effectively connecting them can boost EBITDA, a statistic of operating profit, by 15% to 25%. Although having a 360-degree perspective of the consumer is essential for micro-segmentation and targeting, dynamic marketing campaigns, proactive error correction, and contextualised customer assistance in real-time, just 14% of firms have one.

Data analytics play a significant part in consumer acquisition and retention, with the retail analytics market expected to reach US$23.8 billion by 2027[3]. Although data analytics has many benefits, a few of them are as follows:

Customer acquisition

Customer data analytics provide a broad overview of how a company’s consumers behave and look, which can be helpful in efforts to acquire new customers that are more specifically focused. Companies can strategically plan cross-channel marketing to target potential clients and increase return on campaigns by utilising data science models. In addition, data analytics can assist you in expanding your consumer base by identifying preferences and purchasing trends like demographics, affinity, in-market, remarketing, etc.

Using data analytics for customer acquisition, you can also automate and optimise data feed updates, establish dd groups to target particular items and manage campaigns like thematic search, retargeting strategy, implementation, and ad extensions more successfully.

Other ways that data analytics can assist you with customer acquisition include aligning bidding strategy with KPIs, enhancing Google quality score, increasing online visibility by enhancing local search results and optimising user experience, optimising marketing strategies, and enhancing the effectiveness of advertising by choosing the right attribution model.

Customer Retention

Bain & Company claims that raising client retention rates by 5% can boost profitability by 25% to 95%. By using the RFM (Recency, Frequency & Monetary) Framework to account for acquisition and retention costs, customer retention analytics can assist firms in calculating the lifetime worth of clients. Additionally, it may choose the best attribution model to optimise marketing tactics and boost the efficiency of advertising, construct churn prediction models utilising static and customer engagement metrics, and more.

Better Customer Experience

Retailers may give highly personalised product suggestions, marketing campaigns, product discounts, and other services by using data analytics to segment customers based on shared traits and create extensive profiles for each customer. To plan offers and incentives, you can also determine disinterested customers, possible long-term clients, and frequent purchasers.

Summing it all up!

All organisations want to improve customer experience, reduce attrition, and broaden their customer base, but some succeed more than others. Below are a few pointers to help you make the most of data analytics: Utilize an integrated strategy: Instead of employing analytics in silos, create an integrated plan for growth.

Customers who resemble your key target customers are less likely to churn, so compare potential customers with current ones. Utilize algorithms to contrast the traits and qualities of your current and potential consumers. Utilize predictive analytics to make predictions about what your customers will like or dislike based on prior data.