Utilizing big data for marketing is a practice best suited to B2C. It’s an assumption that many B2B companies struggle to shake off, and reports find that many B2B marketers still fail to find ways to use it effectively.
Fears about the validity, quality and disparate nature of data are legitimate reasons why marketers may be reluctant to put big data to use. However, there are data cleansing, enhancing and processes to unify a customer database to address these issues.
Data governance issues aside, leveraging big data into a marketing strategy can have a significant impact on customer relationships and customer support. It also helps to nurture leads and personalize marketing.
As obvious as it sounds, one of the priorities for marketers is to establish who their ideal customer is. With existing customer data as a starting point, marketers can use insight tools that look at their organization’s market penetration. Then, assess common patterns relating to their customers’ revenue, growth potential, cost to serve, and so on. This identifies a company’s most valuable customers, as well as provide a ‘dream customer’ profile to apply to the wider market, to find others who also fit that profile.
This data insight can also identify promising new target markets, as well as those ripe for cross- or up-sell of services, driving revenue and helping with the acquisition of new and more profitable customers, and quicker.
With lead scoring and lead generation strategies established, big data monitors the progression of prospects and customers through the sales funnel. At the top end, this involves tracking the activities of web visitors to establish their status as a lead, and any possible intent to purchase. For example, this could be the process of filling out forms, downloading white papers and case studies, or registering for webinars as part of the research stage of their journey.
Each of these actions generate data to identify potential leads (to learn whether they conform to their criteria established for ideal customers). Also whether their behavior means they're passed onto sales straight away, or further nurtured by marketing first.
Big data can also tailor the delivery of online experiences, depending on a customer or prospect’s stage in their journey. Marketing analytics tools can segment customers within a database to target with a relevant email marketing campaign. For example, personalization tactics can push them towards content that will encourage them further down the funnel. This could mean recommending a returning visitor to an appropriate white paper, or creating a personalized homepage for existing customers that highlights other services they might need.
Improving the customer experience is a key aim, but deeper knowledge of your customers using big data also means you can cut the cost to serve, while also building loyalty and improving engagement, which will help with retention, too.
Of course, tackling data quality issues will remain an obstacle for those without no data hygiene routine or a 360-view of their customers. Nevertheless, a continued reluctance to put big data to use could see you losing the customers you love, even missing perfect prospects entirely.
This article was first published on the B2BMarketing website
LIVE NEXT at: 10am EDT / 3pm BST on October 5, 2017
Register to watch how to combine customer analytics and real-time, omnichannel customer journey tools to create personalized and contextual customer experiences in this BlueVenn Customer Data Platform software demonstration. In the session we’ll define how to:
- Use predictive analytics to make real-time decisions that positively affect the customer journey
- Improve targeting of campaigns using customer segmentation and RFV analysis
- Use real-time personalization to better engage customers
- Integrate online and offline channels into the BlueVenn Customer Data Platform to create a true Single Customer View