Boost Customer Engagement
Campaign Management systems provide the power and flexibility that marketing organizations need to coordinate and deliver analytically driven customer communications. A real-time multi-channel campaign management solution effectively utilizes the customer touch-points to provide customized offers and increase marketing effectiveness.
We support companies at all stages of the campaign management process, from data preparation to insights generation. We have experience of working with multiple technology providers and can implement a campaign management system to suit your organizational needs.
We support campaign management teams by providing data preparation services, as well as developing guidelines and systems to reduce data preparation time. This enables the marketing teams to focus on their core objectives.
Customer segmentation allows a company to target specific groups of customers effectively and allocate marketing resources to best effect. Dynamic customer segmentation techniques are used to identify the best customers for each campaign.
A/B testing is a great method for figuring out the best offer for each customer. It can be used to test everything from website content to sales promotions. Well-planned A/B testing can make a huge difference in campaign effectiveness.
In-depth reports for Pre and Post campaign behavior provide the insights to continuously improve the performance of campaigns. Dynamic dashboards ensure that all stakeholders have visibility and instant access to their campaign results.
CUSTOMER LIFECYCLE MANAGEMENT
Increase Customer Loyalty
Effective customer lifecycle management (CLM) can enable powerful customer interaction strategies to drive significant business growth and profitability.
We help clients maximize revenue and margin at every step along the consumer decision journey, from acquisition to upsell/cross-sell to loyalty and retention to debt management. We work with clients to analyze the behaviors and needs that characterize their most valuable customers, determine the right objectives (e.g., acquisition versus retention), and identify the best ways of achieving them (e.g., sales and channel strategy).
A unified source for all customer data transformed to provide a 360 degree view of customers, their behavior and campaign responses. The purpose of an ADM is to integrate multiple sources of internal and external data to inform the customer lifecycle identification and decision-making.
Opportunity detection, mapping and prioritization is a critical component of a success CLM practice. This ensures a structured approach to CLM by identifying all business opportunities, mapping these to each customers in the database, and assigning a priority to each opportunity.
Propensity models are used to identify customers who are most likely to respond to an offer, based on their position within the customer lifecycle. Propensity models score all customers or prospects and significantly improve the effectiveness of marketing activities.
Setting up target and control groups is the best way to identify how CLM activity is affecting customer behavior. A number of scientific methods are used to setup target and control groups. This ensures that the results of CLM activities can be clearly evaluated and communicated.
CUSTOMER VALUE MANAGEMENT
Maximize Customer Value
Customer Value Management (CVM) goes beyond Customer Lifecycle Management (CLM) by identifying and capturing maximum potential from prospects and existing customers. The CVM framework uses business, technology, marketing and processes; along with robust measurement systems to help marketers maximize returns from their marketing investments.
CVM shifts focus from managing products or marketing campaigns to managing the profitability of individual customers over the life of their relationship. The core components of the Customer Value Management are:
Customer Lifetime Value
Customer lifetime value (CLTV) is one of the core customer-centric metric within CVM and is defined as the present value of the future cash flows attributed to the customers during their relationship with the company.
Recommendation engines are aimed at identifying the relations between products/services and consumer behavior, using algorithms that make sense of all the information harvested from transactional data.
Optimization models enable marketers to develop an optimal mix of segments, channels, offers, and price points to create measurable return on investment. These models maximize operational efficiency and improve utilization of marketing resources.
Deep Learning is a new area of Machine Learning and typically involves artificial neural networks. Although deep learning models have commonly been used in identifying cyber-crime and fraud, their application in marketing and business is growing.