Customer journey analytics is a technology which enables the marketer to analyze and map customer engagement and interaction with the organization through various channels and touchpoints. The marketer could use this analytics to identify the myriad of paths that customers take during their journey and identify the paths which lead to the company’s growth and profitability.

The competitive landscape analyzes the growth strategies adopted by various key players in the customer journey analytics market. The vendors have been placed into 4 categories, based on their performance in each criterion: visionary leaders, innovators, emerging companies, and dynamic differentiators. The top 25 players have been evaluated in this section of the report. The analysis has been carried out based on specific parameters, and scores have been assigned accordingly.

Vendors who fall into this section receive high scores for most of the evaluation criterion. The vendors in this section have a strong and established product portfolio, and a very strong market presence. They provide mature and reputable customer journey analytics software that cater to a wide range of verticals, globally. They also have strong business strategies. The companies falling in this category include IBM, Adobe Systems, Salesforce, NICE Systems, Verint Systems, Kitewheel, and SAP.

The vendors included in the dynamic differentiators section have the potential to broaden their product portfolios to compete with the other key market players. The companies falling in this category include Quadient, CallMiner, Genesys, and Pitney Bowes.

The companies falling in the innovators section include ClickFox, Servion, SDL PLC, inQuba, Thunderhead, Teradata, and ENGAGEcx. These players have strong product portfolios and robust business strategies to achieve continued growth. They also have innovative products and the potential to build strong strategies for their business growth to be at par with the visionary leaders.

The emerging companies in the customer journey analytics market include Qualtrics, Pointillist, Genpact, UserReplay, QPC Limited, Clicktale, and ForeSee. These players have the potential to build strong product portfolios and business strategies to compete in the market with the visionary leaders and innovators.

Big data has been gaining traction among various industry verticals owing to the accumulation of data over the years. Nowadays, strategic decisions in organizations, especially in multinational and global companies, needs in-depth analysis of big data. This data may span into hundreds of gigabytes or terabytes. According to an article published by IBM in 2016, big data in the form of structured as well as unstructured data created roughly 2.5 billion gigabytes (GB) in aggregate. According to Datameer, a big data analytics solutions provider, almost 56% of the overall customer interactions happen during multi-event, multi-channel journeys, whereas, almost 38% customer journeys take place in more than one channel. In such situations, big data becomes inevitable to identify what is happening across customer journeys and determine why and how such behaviors occurred during the journeys. Eventually, it starts analyzing the customer journeys to gain deeper insights into customer psychology.

In recent past, the emergence of digital technologies has flooded the organizations with a tremendous volume of data. In such scenario, the big data analytics and data discovery empower the users to find hidden correlations among data. Such correlations help to find unknown paths taken by the customer, sentiments of the customer along the journey, and behavior based on certain experiences.

MACHINE LEARNING

The organizations across the world are witnessing transformation with the drastic shift from traditional business models to customer-centric and personalized offering model. Customer experience has become significant for businesses, which help them to differentiate themselves from their competitors. Customer journey mapping plays a vital role in improving customer experience across various customer engagement stages and creates more personalized offerings.

Customer journeys have become more complex with the proliferation of digital technologies. In such scenario, machine learning has emerged as a boon to simplify the picture and identify the drivers of business goals and develop strategies. Machine learning algorithms are used to identify customer patterns from a vast amount of data and predict the customer’s next move. It enables the businesses to analyze every bit of data and identify appropriate programs and offers to lure the customers. For example, machine learning highlights each distinct path taken by the customer to purchase a product and determine the data which is most relevant to the customer journey.

PREDICTIVE ANALYTICS

In the recent past, the customer expectations have been changed drastically, and they consider that the company should meet their individual needs. In today’s world, the customer has more brands with more touchpoints than ever before. The organizations generate an enormous volume of data due to customers’ increased digital engagement. Thus, the businesses and marketers need to have a deep understanding of customers and gather actionable insights throughout customer life cycle to remain competitive. Predictive analytics, which uses historical and current data to predict future behavior of customers, helps the marketers to predict customer behavior and ensure customer satisfaction.

Targeting

In the initial phase of customer engagement process, the marketers need to identify the right prospects. The predictive analytics helps to build an effective list of qualified prospects based on top 4 characteristics viz. geographic, demographic, behavioral, and psychographics.

Geographic: Customer location, and region-urban/rural

Demographic: Age, gender, occupation, and socio-economic group

Behavioral: Rate of usage, benefits sought, loyalty status, and readiness to purchase

Psychographics: Personality, lifestyle, attitudes, and class among others

The marketers prepare the prospect list based on the factors such as a customer who bought the product or service, responded to previous email marketing campaign, or attended a webinar. The machine learning algorithms sort the data and assign certain score to prospect. Based on the score, the marketer generates a targeted prospect list.

Awareness/Education

After targeting, the marketers need to interact with the prospect to cater to their needs and desires. Predictive analytics plays an important role here by creating awareness, educating the prospect, and personalizing the interactions with the prospect based on insights derived from external data or knowledge from previous interactions. The marketer uses predictive analytics to analyze customer preferences and display personalized webpages. Moreover, predictive analytics helps to personalize interactions with customers based on knowledge extracted from business or external data and apply them to customer listings.

Purchase

Once a customer purchases the product, the company should ensure that they remain satisfied. The company should engage in upselling and cross-sell activities to provide better value for the customer and ultimately increase profitability. At this step, predictive analytics helps to recommend relevant products based on demographic data, purchase history, and previous transaction data.

Post-Sales

For a company to grow, the company should remain in touch with the customer, create strategies to retain the customers, and identify the warning signs of churn. Predictive analytics helps the company to determine the best way to remain in contact after customers have purchased a product. It can be done through email, snail mail coupons, newsletter, or a phone call or text messages to notify them about upcoming changes in service.

Predictive analytics helps to identify the customers who are likely to churn and implement retention campaigns by providing incentives. This might include free upgrades or discounted services.