Technologies are scaling laterally and vertically to translate the art of possibilities into real world transformational outcomes. In today’s digital world, consumer expectations are also skyrocketing to experience contextualized and connected lifestyle as product features. This consumer expectation is forcing the product manufacturers to transform from one time sell models to a servitization model. In such a model, products with a pre-defined set of features and capabilities transforms into a platform supporting the fitment in a business process value chain horizontally and contextualization as per customer preferences vertically, while dynamically recognizing the opportunities for upsell-cross sell. Although Platform as a Service offers a fixed set of core features that reflects the characteristics of the product and the USPs within that, it should also provision for helping customers to connect the dots with integration capabilities to products and platforms that deliver complimentary and augmenting potential. The integration capabilities should also be enabling the positioning of the product as a platform service within the industry established and acknowledged value chains. Such a positioning in the industry acknowledged value chain will also open up an assured integration partner-driven sales conversion channel for the product.
Achieving Product Transformation
Product features have to transform vertically by continuously defining the feature USPs and the value chain expansion from product to product as a platform builds up and spreads horizontally. Analytics-led engineering transformation has to be applied for vertical and horizontal expansion as well. An Analytic Ops framework that empowers and continuously accelerates automation and process re-engineering has to be built to holistically drive the analytics-led digital transformation of products as platform business models.
Analytics-led engineering transformation should be focusing on ensuring the best convergence of buying/ usage intent with selling/ stickiness intent. Buying intent should be tracked from the point of a prospect hitting the product/ platform shelf. For that, the product platform should be building the data pipeline and the Analytic Ops for continuously assessing product sentiment, CX sentiment along with competitive market segmentation and prospect segmentation. At the selling intent front, the same market segmentation analysis, usage analysis, sentiment analysis and channel analytics must be performed to have the right product bundling and pricing strategy when the prospect hits the product shelf. Platform/ Product must have the right data lifecycle management backbone to ensure the coverage and availability of right data sets for the analysis to extract the right transformational insights. At each of the decision nodes in the journey of transformation from a prospect to a customer, Analytic Ops should have the right context recognition and devise a context-driven journey for the prospect. Rather than the buying intent traversing in parallel with the selling intent, analytics-driven engineering transformation of the product as a platform should be ensuring the convergence of buying and selling.
Now once the prospect becomes a customer, the Analytic Ops within the product as a platform should have the analytics and data backbone to understand the propensity of the customer for buying more or for a churn. Product as a Platform must include the digital means to understand the context of the customer, not only from the angle of the usage analytics within the platform, but also from the business context angle (expansion – contraction) of the customer to understand the churn, the cross sell and the upsell propensities and then formulating an action with the right kind of pricing, promotional and discount elasticities. Of course, Customer Lifetime Value (CLV) will be a key measure influencing the the right course of action. In today’s world where care support models are transforming from SLAs (Service Level Agreements) to XLAs (Experience Level Agreements), from traditional ones to self-service ones; injecting customer context and customer segmentation within the product as a platform must be ensured to reduce the churn and the causality of churns due to care experience. Product as a platform have to be architected as a self-adapting and self-adjustable control systems with analytics driving the sensing, controlling and the actuations. From an engineering angle of the product as a platform, similar to catching and handling of all the exceptions in order to ensure the desired flow of the program, spreading and distribution of the breadcrumbs for all the user journeys should be ensured for the availability of best fit data sets that deliver precise analytical insights. Self-adjustable and adaptable control system modeling backboned with analytics-led digital transformation should be ensuring the growth at speed as it will be delivering a customer context centric transformation of the business model that would be reflecting in the Customer Lifetime Value (CLV) and the elevation of the customers from the lower segments to Long Term High Value (LTHV) customers. Customer lifecycle segment of Analytic Ops should be ensuring this.
Now let us look at how analytics-led transformation can expand the horizontal product as a platform evolution. Horizontally the product as a platform has to seamlessly connect with partner ecosystem and the legacy ecosystem of the customers to define connected processes that are synonymous with the business objectives. While having seamless connectivity with partner and the customer’s legacy ecosystem, product as a platform should also have the right bi-directional data pipeline to ensure the growth at scale. Partner segment of Analytic Ops should be ensuring this.
An Analytic Ops framework that takes into account the external and internal context of market, customer intent to buy, the sales intent, the CX measures and the CSAT outcome should be powering the backbone of the next-gen product transformation as a platform.
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