4 key concepts for customer analytics

Technology has a crucial role to play in creating seamless experiences between humans and machines, and in a more commercial sense, buyers and brands.

The likes of Blended AI, chat bots, machine learning and more are providing CS agents with more contextual information than ever before combining to enable swifter and smoother experiences for customers.

Many of the principles and technologies being applied to the CX journey have a significant use within the initial sales and marketing activities.

The more you know about prospects and their behavioural patterns, the better you can assist them during the buying cycle. And thankfully for marketers, today’s digital-first consumers are generating vast amounts of information that can reveal the all-important insights that will shape the customer journey.

A typical buyer will interact with a brand or retailer in any number of ways before making their purchasing decision. The more touchpoints and channels there are available to the prospect, the greater the complexity of a potential buying journey.

A timely intervention from a chat bot, a tailored promotion based on predicted actions or the offer of speaking with a customer service agent can all help in converting a prospect into a customer.

The technology for making use of this information and nurturing prospects along journeys is predictive analytics, and there are four key analytical concepts to consider in this realm:

• Collecting customer journey data
• Understanding journeys to personalise
• Anticipating journey outcomes
• Retuning using feedback

Collecting customer journey data

From the traffic on their websites to the mentions they receive on social media, the clues are hidden in plain sight. Collecting this valuable historic data is the first step towards better understanding the factors that will contribute to success or failure in the quest for new customers.

Understanding journeys to personalise

Applying analytics to the amassed information and making use of technologies such as machine learning to understand your customers and mapped out the different journeys. Algorithms can be trained using journeys that resulted in certain outcomes and used to identify the critical moments and interactions that influence the different personas.

Anticipating journey outcomes

Segmenting personas enables the further personalisation of experiences and knowing the optimum intervention point can help you to pre-empt the needs of someone who is browsing your site or engaging on an automated channel.

Retuning user feedback

In a rapidly innovating commercial landscape, customer behaviours are constantly changing as new technologies and capabilities come to market. A continuous learning approach to customer journeys must be adopted, with refinements and optimisations made to processes and strategies.

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Tying these four concepts together needs to be a connected platform that breaks down the siloes between disparate data sources and customer channels, to turn insight into action.

Genesys AltoCloud

Built on a hybrid model, Genesys AltoCloud combines the power of machine learning with an analytics engine to provide the capability to:

• Categorise and segment personas
• Identify important behaviours and journeys
• Plot action maps for human and system interactions
• Drive business outcomes.

You have total control over defining the outcomes, personas and rules. Configuring the outcome probabilities, you can then apply AI to the rules and use scaling bars to set when actions are taken. All of the monitoring and analysis occurs in the cloud, with Genesys managing the provisioning and scaling requirements. leaving you to focus on tailoring the experiences for each persona and outcome.

Increase your conversion rate by learning more about analysing and shaping customer journeys in this white paper.