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Drive Marketing Decisions Using Online Data

Today, companies have access to a large amount of data from multiple sources. It poses a challenge for marketers, who need to identify the most vital metrics as well as actionable insights that are relevant for data-driven marketing decisions. The answer lies in determining the primary reason why the brand exists, and at the heart of that is the consumer.

Understanding the needs of the customers and developing products that satisfy them is the key to successful marketing. Consumer market insights from multiple data sources are gathered to understand the consumers and their behaviour. These sources include demographic segmentation, analysing purchase habits and tracking the response to advertising and marketing campaigns through focus groups and surveys. The use of AI in marketing greatly simplifies the process as large volumes of data are analysed by machines to identify key data points.

Among the ways in which data can help marketers are:

Customer Intelligence

Marketing decisions have shifted focus from giving the customer what the company thinks he wants to a more customer-centric approach that involves knowing who the customers are, understanding their purchase behaviour both online and offline, identifying the channels they use and their expectations from a product. AI-powered digital ethnography goes beyond demographics and presents insights into customers’ emotions and sentiment analysis. Marketers can use this data for better brand positioning or for altering communication strategies.

Spotify uses AI to make personalised recommendations to each of its 72 million customers, who use the service to listen to music without being interrupted by ads. It organises each customer’s music into playlists and curates new music for them based on their tastes.

Realistic Sales Forecasts

In the past, sales forecasts were based on the sales rep’s intuition or gut feel about the chances of closing a deal. AI-powered sales forecasts use multiple data points, including past deals (both closed and failed ones), phone calls, meeting reports and emails. Additionally, since most sales forecast tools are based on machine learning, they become more accurate over time by reconciling the data after analysing the reasons for successes and failures.

Comprehensive Consumer Profiles

Using digital ethnography, companies can capture customer interactions at various stages, providing a better understanding of the profile of existing as well as prospective customers. Based on this, it can ensure the creation and timely delivery of personalised content to the right person, thereby increasing the chances of conversion.

Better Customer Interactions

Another advantage of AI in marketing is that it can analyse a large amount of data, including real-time customer conversations on social media and other online forums. Besides providing more accurate market insights into trends, it also presents the opportunity to communicate with the customer at the right moment through personalised messaging, thereby influencing his decision to buy the product.

Luxury fashion brand Gilt uses mobile push notifications to provide information to its customers about styles or products that they might be interested in purchasing. Based on the AI analysis of past purchases and other specifics such as the customer’s size, the company creates personalised notifications to encourage customers to purchase its line.

Optimised Advertising Campaigns

Whether it’s account-based marketing or digital advertising, AI tools provide marketers with more detailed analysis and insights, based on which they can design the brand’s communication strategy. From analysing social profiles of its audience to keeping track of keyword searches, the access to hidden insights in the large volume of consumer data provides information that can be used to make digital advertising more effective and customer-focused.  Additionally, by knowing which channels are preferred by its target audience, the company can allocate its advertising budget optimally between social media and paid ads.

When The Economist wanted to expand its market by targeting young progressives, it turned to programmatic advertising. By using AI tools to analyse stories that the potential segment was reading and matching it with other data such as cookies and subscriber data, the publication created a series of dynamic ads in real time to match the content and profile of the page viewer. With this strategy, it achieved 50% of the target of 650,000 readers in just nine days!

Dynamic Pricing Strategies

Pricing is a key component in a company’s marketing mix, but the challenge that marketers face is to optimise sales during high demand periods and to offer the right amount of discount when demand is low. AI helps in analysing trends to make suggestions on dynamic pricing to earn the maximum revenue.

Uber relies on machine learningfor its dynamic pricing system. Its pricing strategies are based on the analysis of several external factors such as the weather, holidays, time and traffic.  

Applying Insights for More Effective Marketing

In the current scenario of customer empowerment, marketers need to look beyond numbers and gather customer insights for making better decisions. The use of AI in marketing enables machines to take over the complicated task of analysing the large volume of consumer data, thereby providing marketers with tools that they can use for developing better strategies.