Before diving directly into personalization, let’s go back to the barter system of trade for a moment. In ancient times, sellers and customers had this unique form of trade, where there was a product given in exchange for a product – instead of money. There was a particular seller that sold one type of product and thus the competition was negligible. With an increase in the variety of products and their demand in the market, more than one seller started selling the same product and vice versa.
Coming to recent times, e-commerce has brought in the concept of online business, wherein traders do not have to have products in-store, yet they can have customers and with the help of third-party vendors, supply products to those customers. With the advent of technology and ease of access to it, cutthroat competition has been established in the market. Winning over the same segment of customers with a similar product offering has become an arduous process. That is when the concept of ‘Personalization’ emerged.
Personalization refers to the kind of marketing wherein marketers target a customer and all the further communication is then tailored to the needs and desires of that customer. This makes the customer feel prioritized, taken care of, connected. It thus improves customer loyalty and retention.
Personalization has moved from being an embellishment to a necessity in today’s market.
But to personalize customer experience for every customer, it is imperative that their data is precisely used. Manually finding this data would be like finding a needle (customer profile) from a haystack (information silos). In short, a tedious process.
There is a solution. “Personalization engines“. These SaaS (Software as a Service) solutions can provide insights into customer behaviour, segment similar customers on the basis of demographic and behavioural data and much more. They store data and analyse customer datasets with the help of technologies like Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, and Data Mining.
Let’s dive into the KPIs of these personalization engines and see what can they offer.
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Marketing Analytics:
Marketing Automation:
The core of any organization’s customer-centric workflow is a marketing campaign. It includes communicating to the customer on behalf of the brand via emails, messages, social media, advertisements, and so on. Most of these tasks are repetitive, and thus time-consuming to manually complete.
Marketing automation brings in software and web-based services that automate these tasks while ensuring consistency throughout touchpoints, retention and expansion in the volume of customers, and utilization of more channels. Personalized content including follow up emails, regular invoices and user-specific advertisements can thus be curated and spread with the help of these marketing automation capabilities of a personalization engine.Segmentation & Targeting:
While targeting customers for marketing initiatives, a group of customers with similar behaviour, attributes or properties can be grouped together for large-scale personalization. This is known as segmentation.
It helps organizations deal with the masses while they make every customer feel prioritized. Identification of such user segments can be done by matching digital properties of known as well as unknown customers and their behavioural patterns to the existing customer records. Targeted advertisements using this feature holds immense revenue potential for organizations.Product Recommendations:
For getting a user to buy something, an organization should leverage all the data available on the customer. This data can include demographic data such as the weather details, geo-location, income, gender, age, marital status etc or behavioural data such as websites visited, products purchased, advertisements interacted with, keywords used etc. ML-powered personalization engines, by leveraging this real-time data can formulate personalized recommendations that can turn potential leads into customers, reduce churn and increase sales profits.
Audiences & Analytics:
Knowing your audience is the key to a successful business exchange. Personalization engines aggregate customer data to draft comprehensive customer profiles which also include customer experiences. A detailed analytics report including click-through rate, bounce rate, and churn, formulated by using customer experiences, can give organizations an insight as to where their initiatives are calling to action, where the customers are been driven away, and what could be the next-best-action in real-time. With predictive modelling it is also possible to predict detailed information such as customer purchase behaviour, email response, and the optimum time to reach out to them so they would respond – all this in real-time by adjusting it with live customer data being recorded.
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Personalization:
1-to-1 Personalization:
Every customer has different schedules, different needs, and preferences. In such cases, a one-size-fits-all approach can cause significant damage to the expected results of marketing efforts. Thus, it is important that communication be tailored to each customer by taking note of their active/inactive times, email/offline behaviour, and online behaviour. Personalization can also be enhanced by controlling which product shows up and how they rank in the search of a particular customer. Pop-ups are a little irritating yet an effective way of personalized communication wherein the user can be sent a message relevant to their real-time intent thus boosting engagement.
E-Commerce Personalization:
E-commerce has emerged as one of the largest business exchange marketplaces. Thus it is imperative that organizations go the extra mile to survive and thrive in the vendor competition. The main aim of personalization engines should be to serve the right customer with the right feature/product rather than serving all customers with the organization’s best feature/product. Targeted emails, complementary recommendations, and weather relevant personalized products make sure that the e-commerce engagement experience is enhanced.
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Visualization:
Customer Journey Analytics:
Throughout the journey of a customer with a brand, visualizing all touchpoints to better analyse points of improvement is what a Customer Journey Map (CJM) is. Through precise customer data, which customer is at the risk of churn can be identified and thus additional efforts can be made in that direction. The data from an end to end CJM can help bridge the gap between intended and actual, thus triggering customer engagements. With the whole customer journey in one place, personalized content can be formulated with ease.
Customer Data Platform (CDP):
A CDP helps formulate customer profiles by putting together scattered pieces of customer data from various platforms. This can include both in-store (offline) customer data as well as online data derived from various digital platforms. With a CDP in hand, multichannel, closely targeted customer experience strategies can be formulated which would be in combination with the insights given by the predictive analytics modelling capability of the CDP. With advanced analytics, dynamic customer segments can be created that would adjust in real-time with every change observed in customer behaviour. Having a precise strategy and a unified customer view in hand can make personalization as a process easier for organizations.
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Developer use:
WebOps (Website Operations) Platforms:
A website represents the organization as a whole in front of the customer. The UI of a website is a deciding factor when it comes to customer experience. Thus it is imperative that the UI of the website be agile, interactive, and dynamic. With the WebOps platform, dynamic changes can be made to the website virtually with ease, experiments can be made without putting the website at risk, and activities like deployments and backups can be automated. The presence of a WebOps platform in a personalization engine would facilitate cross-functional collaboration to improve the productivity of the developers and the web team.
ALM Suites:
ALM (Application Lifecycle Management) is a set of processes that are carried out from the stage of ideation to the end of life of the project. Personalization platforms possess the set of APIs and interfaces to enable testing, cross channel data integrations, personalization and product recommendations using the data available. The software development kits in these platforms have the ability to automate triggering and event logic. Both the front-end integration and back-end API-based approach can be combined into a unified testing approach. On quick integration with web analytics and CDPs, these platforms can be leveraged to make a collaborative effort towards personalization.
A/B Testing:
While making the changes in an application, testing algorithms are a necessity. They help to check if the progress is being made in the right direction, how the audience is accepting those changes, and how reversible are they in the long term. A/B tests are statistical tests to check two variants of the same webpage/application, to find out which one works better. These random experiments and tests help in ensuring that the most optimal version comes out as a result and that in turn gives maximum conversions, click-throughs, and revenue.
Feature Management (CI/CD):
Trials and errors are a part of the experimentation process wherein new features can be rolled out or scrapped off based on the audience reaction. Now, frequent changes in the UI makes it difficult for the developer to execute as well as for the audience to absorb. Thus, feature flags help release features gradually and run tests as and when required to measure the impact and roll them back in. Organizations can control which features the visitors see, and via a subdomain, changes visible only to the developer can also be executed. Changes can range from text colour to design elements, whichever works best for the audience.
With personalization engines providing such extensive KPIs, a marketing revolution can be brought around. These AI/ML-driven platforms can enhance conversion rates, click-through rates, reduce bounce rate and churn, deliver personalized journeys, create a consistent brand image across channels, and a lot more.
