Consumers are increasingly vocal about their values and preferences. A recent Salesforce report revealed that 80% of customers say the experience a company provides is as important as its products and services, and 76% say it is easier than ever to take their business elsewhere. It’s vital for brands to listen to the needs of their audience and understand the “why” behind that need in order to connect with them and keep them engaged. By utilizing data to understand consumers and their tastes and preferences, brands can increase customer acquisition and retention while reducing expensive customer churn.
Intent data is the collection of behavioral signals that predict a consumer’s intent to buy a product or service. Intent data is essential when it comes to efficient and effective acquisition.
Intent data is generated by a person or company when they interact with content or do research online. Web activities such as content downloads, webinar registrations, keyword searches, and comments can provide key datapoints that signal interest in a product or service.
Brands use intent data to find high-quality leads, improve personalization, and prioritize prospects or business accounts based on their interest in specific products or services. In addition, intent data can be used to find where prospects are looking for content related to the brand’s offerings. Some brands have seen more than a 79% increase in close rates and a 16% increase in engagement using intent data because it helps brands deliver the right product to the right customer at the right time.
Brand Example: Dodge Data & Analytics
Account-based marketing (ABM) is a major trend in the B2B space – and marketing and sales teams are using intent data to prioritize accounts that are specifically looking for their types of products and services. Construction intelligence company, Dodge Data & Analytics turned to intent data to help with snagging enterprise accounts. They started by incorporating intent data into their segmentation strategy. The accounts were sorted by level of engagement, which would trigger different marketing campaigns, with messaging tailored to where each customer was in their journey.
Dodge Data & Analytics also used intent data to hyper-personalized their display ad and website messaging. Display ads would show a key account a data point about the targeted account, pulled from Dodge’s database. For example, the data point might show that the account is the 13th largest construction company in the tri-state area. If a prospect working for the targeted account visited Dodge’s website, they’d see the same data point they saw on the display ad on the website as well. Dodge saw their re-engagement numbers for target accounts rise significantly, transforming cold accounts into warm ones.
A persona is a reference to a collection of attributes that represent a segment of a brand’s audience. A persona encompasses that segment’s demographic and psychographic attributes, behaviors, motivations, and purchase patterns. Brands typically have a number of customer personas that represent various customer segments. A well-constructed persona helps brands better understand their target audience so they can tailor products, customer experiences, and marketing messages that resonate. Many brands give their personas names to help humanize their target audience.
Personas are modeled on qualitative and quantitative data. Many brands use to create personas using information gleaned from preference centers, polls, web behavior, email activity, purchase history and more. However, zero- and first-party data alone usually won’t provide enough information to create a 360-degree view of a brand’s customer. That’s why many brands also use third-party data collected by a data provider through various platforms, applications, and websites.
Brands frequently use personas in their marketing strategy to tailor messaging to different audience segments. For example, brands can create unique landing pages to highlight product features that appeal to each persona or use personas as a way to inspire and guide personalization for marketing campaigns. Research has shown that brands using personas can increase their website effectiveness 2-5 times, improve conversion rates by 10% and grow sales leads by 124%.
Brand Example: Thomson Reuters
B2B brand Thomson Reuters invested in data and technologies to improve their marketing and sales efficiency. They created buyer personas, such as Indirect Tax Analyst, Senior Tax Preparer, and Manager of State and Local Tax. That helped the brand tailor their messaging across email, display search, web, video, and mobile. The brand’s buyer personas contributed to a 23% increase in number of leads sent to sales, a 72% reduction in lead conversion time, and a 175% increase in revenue attributed to marketing.
Over 90% of global consumers say that customer service is crucial in selecting a brand to address a need. Brands need to use data to discern how their audiences want to connect and invest in technologies to optimize those audiences’ experiences.
Data allows companies to respond to customers faster by powering technology that allows customer service representatives to pull-up a customer’s purchase history and profile to help identify the customer’s issue more efficiently.
Artificial intelligence is changing the customer experience landscape. AI-powered chatbots are being used in new and innovative ways to help consumers along their purchasing journeys, as well as providing brands with invaluable insights into their customers. Chatbots collect first-party data directly from consumers through direct questions. Companies can then take that data and use it to optimize messaging or adjust their personas.
Brand Example: Whole Foods
High-end grocery retailer, Whole Foods, uses an AI chatbot to help customers with everything from meal recommendations to recipes. Customers can message the Whole Foods bot through Facebook Messenger. The purpose of the bot is to allow customers access to a virtual chef who would help them answer the eternal question, ‘what’s for dinner?’ The bot provides customers with recipe ideas based off keywords or emojis. Once the customer selects a recipe, the bot generates a grocery list, and then points them to the closest Whole Foods. The chatbot not only aids customers, it provides Whole Foods with invaluable information on who their customers are – including which cuisines they are most likely to search, what items they buy when they’re making dinner, their ‘starting point’ meal items, etc. Whole Foods can use that data to personalize emails to their customers, reminding them it’s time to restock on a favorite product. They can also send emails that feature gluten-free or vegetarian options if they know the customer frequently searches for those types of recipes. By staying ahead of their customers’ needs, Whole Foods can delight shoppers while also improving their bottom line.
As the insights gleaned from data become more sophisticated, brands can create more value for their target audience and become an integral part of consumers’ lifestyles.
Did you know Infogroup now offers intent data capability? Read more here.