Every stage of the customer journey has ‘an experience’ in the wait. Be it the customer calling the customer service team to find answer to an issue or a customer making a research to find the best price for a product, creating delightful customer experiences is about creating lasting impressions on the customers, from a price, product and service perspective.
In enabling great customer experiences, organizations bank on internal and external data to unearth insights that can help promote engaging customer experience. Data from various sources – mobile, social, applications, email, campaigns and other sources – is waiting to be tapped using advanced analytics and machine learning to acquire insights in unlocking enriching customer experience
What are the top analytics keys to enable rich customer experience?
Predicting customers’ purchasing patterns from their past behavior
The need to facilitate enriching customer experience is augmented by predicting behavioral patterns, preferences and customer needs. Analyzing past behavior data helps in recognizing present customer needs and in predicting the product that a customer would want to purchase. This is well supported by analytics tools as well as recommendation engines to be proactive in recommending related products that customers are looking for. Demographics of the customers combined with their purchase history enable prediction of what products customers will buy.
Acting on real-time intelligence
Predictive Analytics also paves the way for real-time results as well as intelligence, which guide in embracing robust strategies to enable enriching customer experience. It is about connecting to streaming and real-time data, unearthing meaningful patterns gaining insights from customer interactions. Finally, it is about taking prescriptive actions to act immediately and enhance customer experience
Leveraging customer analytics for personalized services
As per Forbes, 85% of mobile marketers have gained from higher engagement, more conversions well as higher revenues by way of offering personalized services to their customers. A robust personalized marketing strategy resulting from existing data can be effective. For instance, collating data ranging from channel preference, visit frequency, product preferences and leveraging analytics will help in rolling out personalized marketing and in recommending product meeting customers’ tastes and preferences. Personalization is made possible by leveraging varied and disparate data points including location data, region, and behavioral data, preferences and interests and channel data.
Using optimized pricing models
Price optimization is about interpreting customers’ responses to different pricing strategies – made possible by using data analytics techniques and finding the best price for the company. Pricing models take several factors into account as that of costs, competition, season, etc. While pricing of a product can be impacted by several factors, machine learning models built out of relevant datasets can pave the way for robust pricing models and help make optimized pricing decisions.
Connecting to customer touchpoints & Analyzing multi-channel data
Customer data needs to be collated from various internal as well as external touchpoints, and analyzing all the touchpoints becomes a crucial element in facilitating enhanced customer experience. Collating and analyzing data from various touchpoints encompassing pre-purchase, purchase as well as post-purchase points guide in knowing customers’ opinion as well as feeling about a company as well as products. With customer interacting with a brand through many devices and channels, it pays to capture data from all sources like mobile, kiosk, email, campaigns and other applications to analyze customers’ behaviors across customer journeys.
Leveraging advanced data analytics, retailers gain better understanding of customers, preferences and behaviors, which helps in facilitating great customer experiences. In terms of personalizing customer interactions, artificial intelligence gives the lead in enabling personalized customer experience, by using relevant data around the customer and offering rich insights about the customer.