We deploy Predictive Customer Analytics solutions that help Clients better anticipate and respond to customer demand, churn and loyalty insights. The result is improved retention, revenue and profitability
Analytics to optimise Customer value
Customer analytics at Trusted Data takes clients beyond traditional insights towards predictive and prescriptive intelligence….How will customers respond to offers, what offers to make to them and when? These are some of the key attributes that we try to define through our analytics work.
Our reach through customer analytics is manifold, as we adapt our solutions based on what business outcome our clients ultimately seek by heightening understanding of existing and potential customers.
How Ai can Improve Customer Acquisition
Trusted Data’s approach within customer acquisition analytics seeks to deliver measurable performance step change for our clients.
Our key impact areas include improving the rate of customer acquisition, lowering the cost of customer acquisition, increasing customer conversion rates, increasing decision / purchase turnaround speed, increasing purchase size and improving ROI from marketing efforts.
Customer Risk Analytics
How we innovate in Customer Risk Analytics
Risk can be defined and measured in a variety of dimensions so Trusted Data adapt their risk analytics solution methdologies based upon the clients’ sector and challenge.
In terms of compliance, we use intelligent analytics and Ai to support Clients in combatting risk and fraud.
In B2C sectors, including telecoms and other subscription / tariff based pricing models, we deploy innovative analytics approaches across customer retention and churn to mitigate risk, when customer experience issues, pricing issues, inadequate campaigns or competitor offers pose our clients ongoing threats.
What is Customer Value Analytics
Inferring customer value is critical, particularly in B2C sectors, where repeated customer touchpoints not only generate huge volumes of data but also offer various opportunities to uplift the customer journey through intelligent analytics.
Trusted Data excel by supporting our Clients to derive customer long term value and appropriately segment their customers, ensuring that the right customers are targeted with the right products / promotions at the right time.
Targeted Campaign Analytics to driven Customer Engagement
Today’s multi-channel business environment presents many opportunities and platforms for engagement. The result is a rise in marketing costs and a decrease in ROI visbility / optimisation.
Marketing functions face significant pressures to mitigate spend. Targeting consumer at the right time, through the right channel is at the crux of enhancign campaign management, through data and Ai.
Trusted Data’s unique campaign analytics solutions focus on careful selection of audience and channel data, to deliver outcome-based improvements in campaign design, pricing and offer redemption.
Predictive Customer Analytics
Most organisations depend upon traditional enterprise data warehouses and BI solutions to drive customer analytics. The problem is that this infrastructure cannot handle unstructured data and it cannot run sophisticated analytics such as click path analysis, advanced data mining, clustering or prescriptive recommendations.
Trusted Data focus on developing customer analytics solutions, integrating multi-channel data and conducting iterative data discovery, all focused on delivering insights and actionable intelligence to deliver a growth in revenue, conversion, retention or margin.
Customer Lifetime Value
Calculating Customer Lifetime Value (CLV)
Trusted Data’s approach to customer lifetime value dynamically tackles customer profiling from a range of different angles.
This includes observing customer churn propensity (to measure long term revenue realisation from each specific customer), customer purchasing habits (as historic CLV does not denote Future CLV), purchasing frequency and customer type, the latter of which requires very careful segmentation using advanced classification and clustering algorithms.
Our Machine Learning driven CLV solutions offer clients the ability to nimbly assess and respond to a changing customer landscape.
How Trusted Data Apply Customer Segmentation
Trusted Data primarily use customer segmentation analytics as a critical input in areas such as pricing and campaign management. Knowing customer segments is only part of the puzzle…Knowing what to do with this information is where our Ai solution capabilities add real value as we can customise our clients’ approaches to:
- Identify customer profitability
- Identify underserved / high potential customers
- Optimise channel-specific marketing investment
- Predict future purchase patterns and generate pricing accordingly
Trusted Data acheve this by tracking, measuring and segmenting multi-dimensional customer attributes including motivation, purchase habits, price sensitivity, satisfaction, network recommendation propensity, purchase frequency and mode of transaction.
Customer Churn Analytics by Trusted Data
Successfully reacting to customer churn threats in a pro-active manner is a business game-changer.
Trusted Data have extensive expertise in combing through a myriad of data to calculate and predict if a customer is most likely to churn in the near term. Our churn analytics solutions are industry agnostic and include:
- detecting account closures in banking
- policy closure projections in insurance
- subscriber churn in media and telecoms
We apply numerous analytical techniques in doing this, including building customer scorecards with multiple logistic regression models and decision trees to accurately predict churn.
We support our clients to better understand customer drivers and refine:
- customer messaging
- special offers / discounts
- steps in the customer journey
- efforts to customers at risk
Data-driven Customer Loyalty Analytics
Loyalty-based analytics focuses on fostering long-term, mutually beneficial relationships that move organisations away from segmentation-driven campaign management. To deliver loyalty analytics at scale, clients must turn to Ai.
Trusted Data employ Ai and Machine Learning techniques to allow our clients’ loyalty initiatives to effectively perceive, classify and directly engage with their customers.
We model a range of attributes (and their sub-components) including retention metrics, customer advocacy scoring and purchasing habits to:
- Personalise communications to at-risk customers
- Personalise marketing communications
- Identify the right products for ready-to-buy customers
- Encourage recommendations
New Customer Acquisition
Use Data to Acquire New Customers
New customer acquisition has been a costly exercise for most firms. Blanket approaches to business development lead to generic campaigns that often do not elicit an adequate response from customers.
Let’s briefly look at the facts from a leading industry research report:
- Companies adopting predictive analytics realise 10% annual increase in new customer opportunities
- Those companies adopting predictive analytics increased total customer numbers by an incremental 35% relative to those not using such approaches.
- Companies applying predictive analytics were nearly 3 times more likely to cross-sell and upsell than non-adopters.
At Trusted Data, we look at customer acquisition both in terms of enticing new customers to your base and to renew relationships with existing customers. Using a suite of data intelligent approaches, including segmentation, upselling and dynamic pricing, we ensure that our clients better understand and respond to present / future customers with the right offers at the right time
Using Data Analytics to Enhance Customer Retention
Customer Retention is a phenomena explained by a range of insights, including churn, loyalty, segmentation, CLV and sentiment.
Software applications struggle to understand this, especially when a range of structured and unstructured data needs to assimilated and modelled to form an integrated perspective that drives data intelligent decision making in retention programs.
Trusted Data combine the benefits of a cost-efficient approach with advanced data science techniques and applications, to run multiple iterative models, capable of rapidly scoring millions of customers to drive more informed insights and decisions.
We use data to help our clients better understand why customers are leaving, why customers purchase certain products, who their customers are, what common attributes are shared between hi/lo value /churning customers and what custom offers will entice customers to stay.
Our clients benefit from optimised portfolio management capabilities, improved understanding of ideal customers / products / offer bundles, whilst minimising unnecessary marketing investment.
Customer Cross Selling
Delivering Next Best Offers to Customers
This complex analytical domain often relies on the careful integration of predictive and optimisation analytics to better inform future customer purchase propensities.
Trusted Data employ a range of analytics approaches, including pattern and market basket analytics to predict the next best offer.
In banking we have deployed such solutions to provide more relevant banking product offerings to customer segments at the right time. In telecoms, we have used related anlytics applications to improve customer retention and loyalty initiatives. In retail and consumer goods, we have applied optimisation analytics approaches to improve assortment and promotional offers, on shelf and at POS.
Customer Behaviour Modelling
Customer Behavioural Analytics
In a digital world, customer personalisation and experience separates winners and losers. It is now becoming increasingly difficult to compete in any industry, where companies are too slow to understand and react to customer trends, purchase behaviours and sentiment.
Trusted Data’s approach to customer behaviour modelling goes far beyond demographic or qualitative interpretations, by observing and predicting individual (rather than global) attributes.
By deploying recommendation engines, for example, our clients can offer their customers dynamically personalised content and product suggestions based upon each past and predicted behaviour.
Our approach to customer behavioural analytics is centred on just more than understanding ‘who customers are’ and more towards how to deploy intelligent Ai solutions that re-engineer experience, offers and pricing to maximise customer value.
Leveraging Sentiment Analytics for Customer Intelligence
With many industry sectors have high customer / consumer visibility through a range of online channels, responding to shifts in customer sentiment can have a signifcant effect on reputational risk, brand value and sales.
Trusted Data support our customers to mine text data to build accurate insights into customer sentiment, positive and negative, to better inform customer service and campaign efforts focused on increasing customer loyalty, customer retention and customer awareness.
We segment sentiment data at a range of sub-levels, including by location, channel and brand to help clients identify opportunites and threats. We enable clients to also track sentiment shifts so they can develop a relative understanding of sentiment against market leaders and emerging players.
We use a range of NLP analytical techniques to deliver beyond basic sentiment classification, offering our clients more qualitative understanding of customer / market sentiment.
Advanced Analytics to Compute Customer Profitability
Armed with added data intelligence, some companies are able to increase the profitability of low-profit customers and retain their most profitable customers.
Trusted Data use analytics to help our clients identify the customers who best fit their business priorities (i.e. sales, LT value or cashflow).
We use analytics to segment customer profitability based on factors such as historical value, current value, future value and cost-to-serve. We use these insights to model customer-specific outcomes for our clients, which largely drive refinements and customisation in segment-based pricing, retention scheme design and customer experience management.
How to Develop Customer Recommendation Engines
Though made popular through online trade and media brands, the science behind customer recommendation design is very technical in nature.
Using a range of analytical techniques, including predictive, optimisation and classification algorithms, Trusted Data engineer recommender engines based on the Clients’ trading model(s) and objectives.
Our recommender engines can be configured for personalised product recommendations, site optimisation, real-time notifications and loyalty design, integrating a range of data, including:
- user behaviour
- user / cluster preferences
- product-level features
- merchant events (i.e inventory status, promotions and sales)
- contextual data (i.e. device and location data)
Our core objective with recommendation engines, is to integrate data and insights for our clients so they are equipped to drive customer loyalty, retention and acquisition.
Customer Risk Analytics through Ai
The definition of customer risk varies in application by industry.
Trusted Data have worked with clients where risk is measured against credit worthiness, churn impacts and compliance threats.
As each company’s requirements in customer risk management vary, Trusted Data focus on delivering increased customisation and relevance through analytical methodologies that best suit our clients’ investment appetite, business scope, data complexity and business objectives.
We are adept at developing risk engines that better inform operational decision-making, allowing our clients to anticipate and respond to credit management threats, AML / Fraud and revenue at risk more effectively.
Our Approach Behind Customer Analytics
Define the CUSTOMER INSIGHTS CHALLENGE & target performance outcome
Clients’ target customer analytics efforts require measurable performance to evaluate analytics impact.
Trusted Data support our Clients is selecting the right data, modelling the right outcome to deliver success in customer analytics.
ACQUIRE THE right data & ASSIGN THE RIGHT solution methodology
Given the variety and scale of data available in consumer markets through media and transactional platforms, data selection and methodology design are critical success factors in customer analytics.
Trusted data work closely with our clients in this domain to avoid the confusion emanating from black-box approaches.
Model & Deploy the right proprietary solution to achieve your TARGET outcome
By utilising AI & Machine Learning, we offer Clients added analytical horsepower to comb through the myriad of data which they warehouse or can acquire from online mediums, to extract the right customer insights and outcomes across the customer life-cycle.
Our solutions are adapted to meet the needs of our clients now and in future.
Knowledge Transfer, Solution adaptation & impact tracking with the client
We remain to close to clients to support effective knowledge transfer, solution impact tracking and iterative refinements to solutions beyond Pilots or version 1.0
Client needs change and Trusted Data remain repsonsive to adapting solution functionality in line with such requirements.
How We Set Ourselves Apart
AI Driven Customer Analytics
Trusted Data do not operate in the same manner as traditional consulting or software firms. We rely on combining deep domain knowledge of anaytical techniques with the most relevant and advanced analytics tools. The results are adaptive solution methodologies that syncronise with the Clients’ specific customer insights objectives, delivering greater insights and recommendations in less time.
- Flexible pricing (gain share or result driven)
- Right| Data, Methodology & Modelling
- Open source tools
- Achieving target for you
- Speedy delivery
- Guaranteed ROI
Some of our Customer Analytics Solutions
Our specialist team of Data Scientists and Customer Analytics experts have delivered a suite of sophisticated solutions, supporting customer intelligence / acquisition / loyalty / retention efforts for our Consumer Industry clients.
Computing Risk in Banking
Dynamic risk computation & classification
Telecoms Customer Acquisition
Revenue growth through customer acquisition
Retail Promo Optimisation
Personalising customer promo offers at POS
Loyalty Analytics in Banking
Revenue growth through loyalty analytics
Customer Churn in Banking
Churn analytics and prediction in Banking
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