AI Use Cases 

Find better segmentation modelling to allow for more appropriate customer management (e.g. offers, marketing techniques, life-time value management). Identify "tribes"

Model, determine and monitor the sentiment of customers across touch-points to identify general trends of satisfaction

Leverage machine vision to tag brand-related and product images taking into account users' preferences and relevant media channels.

Automatically tag content for library and customers: using automated image and video recognition, along with text analysis automatically recognise, classify and tag content assets and media (e.g. find all images of the product. Or tag major activities in videos).

Enable Augmented Reality (AR) sales propositions by geo-locating product against a moving image - often generated via a camera phone. The AI places the product in the image - for example furniture in a room or make-up on a face. This may often be a 3D image.

Predict local or regional sales and demand trends using up to date (with the ambition being real-time) data

Analyse business indicators to forecast likely demand ranges for products and / or services.

Measure value of earned social media of event brand and product sponsorship

Capturing data on key life state events - from ageing or birthdays to more profound events such as house moving or bereavement - enables companies to target specific products or services. This data can be uncovered from multiple sources (within relevant legal constraints) and then the running of specific campaigns can be automated.

Use facial tracking to predict identity - and potentially emotional reaction to service proposition - to support targeted customer offerings.

Determine the likelihood of reactivation and with what techniques.

Automated generation of market research questions and deployment across digital channels, Capture and visualisation of responses.

Identify which ad marketing promotions and campaigns are most effective in driving customer traffic, engagement. Use attribution analysis to optimise marketing mix.

Automate sales voice conversations through a conversational agent enabling potential for simplified, high-volume sales. If inbound this can be a valuable consumer experience reducing waiting time but outbound and unsolicited it can be legally questionable (and a most unpleasant experience).

Determine the optimum product and service mix for maximising LTV and reducing churn. Determine optimum packaging of products.

Context helps with marketing. So emotional states triggered by relevant content will impact on marketing effectiveness - so football on TV attracts specific product categories for example. What should go where can be micro-optimised using AI.

Predict email open rates and click rates

Create content optimised to customers and consumers, driven by data captured from multiple inputs ranging from commercial process stage (e.g. discovery or closure), web history or third party data sources.

Personalise strategy to target individual consumers based on multiple data sources (mobile, social media, location, etc.)

Capture optimal cost per acquisition KPI through marketing mix and marketing spend analysis and execution. Typically simplest to execute through digital channels.

Automatically generate content for marketing materials

Connecting marketing data and KPIs automatically potentially empowers cross-functional team working (and broader management buy-in) through enabling stronger data visualisation.

Forecast demand to ensure product and service availability to limit out of stock or over stock situations for new product introductions.

Measure and better predict the effectiveness of different techniques to cross and up-sell (e.g. sales, marketing, merchandising) based on historic purchasing and browsing behaviour along with cohort analysis

Determine the likely lifetime value of customers to allow better customer management and engagement to increase revenues. It will allow a firm to better target its marketing resources to ensure that customer acquisition costs are managed appropriately by relevant segment.

One of the fastest ways to build revenue is to increase average shopping cart size and to cross-sell to exisiting customers with whom you have a relationship, data and a brand affinity. Also called recommendation engine or recommendation system, these systems leverage customer data to ensure that existing customers purchase more.

Retargeting customers who have already expressed interest in products or services. Typically the drop-out rate whilst filling in personal details (e.g. payment card information) is high - but these are the most valuable of convertible leads for most online organisations.

Combine product information from disparate sources, structuring the information centrally for internal use to improve product discoverability and supplement decision-making.

Measure and better predict the characteristics of target customers for different products and service offerings.

Product rationalisation or retirement determines which products to divest or retire, and when, based on that product's impact on customer value and inter-related profitability of other products (market basket analysis).