The marketing landscape is continually evolving. It is no longer about pushing out a list of features and an attractive pricing list for a product or service. It now focuses on being more customer centric and understanding how they want to interact or what they expect from the business. With face to face conversations with thousands of consumers not being a practical approach, technology steps in to help marketers leverage the large volumes of data available online.
Whether the goal is to get 360 degree view of a customer to feed him with more personalized promotional offers at the right time and through the right channels, or to reduce the overall marketing spend, technology is stepping in everywhere. For instance, we have worked with several Fortune 500 companies, advertising agencies and consulting companies to help them implement big data using technology, to cater to their day to day marketing needs.
The end goal of all these businesses - big or small, is always to optimize their marketing mix, drive sales and redefine customer relationships.
When there is a new social platform being launched every other year, your marketing efforts are sure to spill over in the zeal to reach out to your customers before anyone else does. While catering to the latest fad isn’t a bad idea, spreading yourself too thin can actually lead to losing focus on the one channel that is leading to actual customer conversations.
Marketing analytics helps you measure the success of your marketing efforts. Typically, the project is broken into the following segments:
I. Customer segmentation and profile
Customer segmentation helps you optimize and allocate resources based on their value to the organization. This includes making use of both demographic segmentation data and advanced clustering segmentation to offer actionable marketing analytics. This is especially important when you’re looking to determine new product offerings or develop a personalised marketing campaign.
II. Personalized and customer driven marketing
Once you know who your customers are, what they are looking for and how they choose to interact, the next step is to put all the data on your marketing analytics dashboard, to use. That’s called personalized and customer driven marketing.
You have heard of how important personalizing advertising campaigns has become. With contextual marketing taking priority, it is has become important for businesses to dive into data. Businesses are now using machine learning to set up their multichannel marketing campaigns. This helps the organizations track real time events, social interaction, online behaviour, dig deeper into historic data and create dynamic customer profile to create data driven campaigns that focus on offering a great customer experience.
But apart from being able to offer an impeccable experience to their customers, it also helps businesses predict their marketing campaign’s performance and optimize them for better results. The Accenture 4R personalization framework explains this the best.
III. Marketing mix and ROI
Using predictive analytics, organizations can quantify the potential value of all marketing inputs and identify those that are most likely to produce long term revenue growth. The understanding of marketing ROI for each offline and online media channel, helps in better allocation of resources and smarter execution of campaigns.
IV. Attribution modeling and performance attribution
As you run various promotional marketing campaigns - email, search ads, social media ads, display ads and offline promotions like TV, radio, print, etc., it is very difficult to know which of the channels bring in qualified conversions and sales. This makes it difficult to optimize your marketing budgets.
But with attribution modeling, marketers can identify what triggered a response on every channel - getting a better understanding of which of their marketing efforts brought them the best of results.
V. Product portfolio management
Optimizing your product mix based on the dynamic market needs is the only way to maximize your revenue potential. We have worked with organizations to help them understand which product combinations are being purchased together, the likeliness of this customer to come back for another purchase and also identify information that can be used for upselling or cross selling campaigns, and making product recommendations.
The regular optimization of product portfolios enables an organization to identify the strengths and weaknesses of particular products in specific market segments.
VI. Brand equity
Brand equity refers to a set of brand assets and liabilities linked to a brand name. They symbolise the value their product or service brings to the consumer. The equity of a brand has several dimensions like brand awareness, brand image, customer perceived value and brand association.
By implementing technology that leverages data, you can integrate customer survey data and predictive modelling. This will help identify optimal routes to build strong brand equity and help you achieve a target market share, customer acquisition, brand loyalty and other desirable outcomes.
Like we said before, helping the customer and adding more value to them, is what converts higher than a discount. The other part of technology driving marketing is the customer. Diving into customer analytics, helps you understand your customers better, analyse their next move, create personalized experiences for them, to win more business and drive loyalty.
I. Customer profiling
Whether you’re just starting off or pivoting to launch new product offers or reworking your marketing campaign, knowing who your customer is, is important. It also helps you identify low profit and high profit customers to identify which segment needs more attention.
To be able to do this, you need go beyond demographic segmentation. Using advanced clustering segmentation techniques, organizations can unveil meaningful and measurable segments as per customer needs, behaviors and social profiles. Knowing your customer on an almost 1:1 basis definitely helps in tailoring your products and services to suit their needs better, and customize your marketing message for better performance.
II. Customer lifetime value
Understanding the customer life cycle and customer lifetime value, offers profitable benefits to the organization. It helps in smart customer segmentation, campaign prioritization and smart personalization. Focusing on customer lifetime value helps you in business forecasting and improving the health of your company with targeted marketing and higher retention rates.
III. Customer churn analytics
The customer acquisition costs are increasing by the day. Considering the competition in the market in terms of businesses offering similar products and services, reducing customer churn is the only way to ensure sustainable growth.
To be able to do this, you need to dive deep into your customer data, segment them based on interactions and purchases and analyse who is closer to churning due to lack of engagement or dissatisfaction. This enables you to implement churn prevention strategies, upsell and cross sell before they walk out of the door.
IV. Cross selling and upselling
When you have a system that identifies and highlights valuable customer interaction data, you can identify customers who have a higher chance of making additional purchases. This data enables you to basically target these customers with innovative and personalized cross selling and upselling strategies - both of which, reduce your acquisition costs, increase average order value size and increase the customer lifetime value.
This is where our experts use the acquisition pattern analysis and collaborative filtering. They make use of the algorithm to analyse the patterns in the customer’s past behaviour, correlate it with similar target markets and then identify product promotion opportunities.
V. Market basket analysis and product bundling
With market basket analysis, our experts can help you understand which product bundles are the most popular amongst your customers and what sequence they make these purchases in. By mapping your customer’s journey, you can use the data to make the right recommendations, cross sell and upsell products and even offer coupons that they are most likely to use.
The best example of the importance of a personalized product bundling, is Netflix. The company offered a $10 million prize to anyone who could improve its recommendation engine.
Businesses have already made use of advanced algorithm techniques to build recommendation engines that outperform even Netflix.
VI. Recommendation engine
Understanding the preferences and intent of each visitor and customer, helps you target potential customers with the right message at the right time, via the right channel. We have worked on setting up custom product recommendation engines that determine the ‘next best action’ for each customer at any point in time.
The technology uses a model that captures life-event patterns, buying behavior, social media interactions and other aspects. Knowing where to approach which customer with what, you can create the right experience and win loyalty.
VII. Sentiment analysis
Combining machine learning and artificial intelligence, you can understand the tonality of the conversions you get on campaigns - positive, negative or neutral, with sentiment analysis. Using text mining and analytics, it helps you analyse an unstructured customer feedback across multiple communication channels - email, call center, surveys, social media.
Opinion mining helps an organization get insights into what their fans, following and customers think of the brand. This helps in optimizing the brand messaging and marketing campaign messages for higher relatability, leading to increased conversions.
Simply put, from where we are standing, we are seeing businesses adopt technology to leverage consumer data for their growth. With data, we will not just see marketing and sales campaigns become more data driven, but also focusing more on customer experiences.
Have you been leveraging technology to fuel your marketing and sales, or are you still in the 43% of businesses that are struggling to implement them effectively?