Marketing has not escaped the shift to a digital, data-dependent economy. Most critically, marketers can no longer rely on gut feelings (or “I think this will work”) to play a dominant role in strategy.
Here are just a few eye-opening statistics: Marketers’ number one challenge is understanding customer interactions across all touchpoints. A Forbes survey showed that 88% of marketers use data obtained by third parties to better understand each customer; 66% use marketing data to enhance targeting offers, messages, and content; and 33% of top marketers say the right data collection and analysis technologies are most useful in getting to know customers.
With more data than ever before, today’s marketing teams prove their value to the rest of the company by leveraging this information — by integrating deep-dive analysis of accurate, real-time data into marketing strategies — to derive insights that help them finely tailor marketing efforts to customers’ pains and expectations.
The Many Ways Data Science Matters to Marketers
Marketers deal with these three types of analytics to make smarter decisions to achieve their goals: Descriptive (historical information), predictive (possible outcome based on historical or real-time data), and prescriptive (recommendations that helps take advantage of what’s likely to happen). With this data and analysis on hand, a company can:
- Personalize customer interactions, making them feel valued, and not just a statistic.
- Plan and refine content and measure its results.
- Focus marketing efforts toward the segments and locations that give the highest ROI.
- Discover which customers are high- and low-value — and adjust strategies accordingly.
- Know customers’ preferred channels, as well as which specific content, posting day/time, or partnerships bring in optimum response.
- Learn, through social data analysis, what customers really think about your business or marketing campaign and even their competitors.
- Master customers’ buying cycles to determine the optimal times for a marketing push.
- Know how to better allot a budget to each campaign, channel, and location that best delivers results.
- Enhance buyer persona research to go beyond mere demographic descriptions.
- Improve pricing strategy and pinpoint exactly what drives prices: The economic environment and the customer’s history with a brand, including past price negotiations, willingness to buy, etc.
How Technology Can Help
Does all the above seem overwhelming? If you’re a B2B marketer tasked with tapping all the available data, expertly analyzing it, and pinpointing where trends are headed to improve business decisions, it certainly seems that way.
It’s time for you to take control and use technology to make it manageable.
Consider Tipalti, the leading supplier payment automation provider. Tipalti’s go-to-market strategy is built around target lists for Account-Based Marketing (ABM). They needed to:
- Fill their pipeline with qualified accounts.
- Identify the handful of key individuals within those accounts to engage with.
Traditional lead and account list vendors can provide net new accounts and leads, but they’re very blunt objects and generate a huge amount of extra work for marketing. Typically, a significant portion of the data will be inaccurate, out of date, incomplete, or duplicates of prospects you already have. That means hours of extra data cleaning and management, as well as wasted time, money and effort on bad data.
But data — critical as it is — is merely the fuel that can power marketing strategy. To make a strategy actually run, the raw data needs to be processed (deep-dive analysis) and turned into data intelligence. A customer data platform (CDP) can do the job. A CDP aggregates many data sources — bringing all individual and company-level data together — and “scrubs” them to provide clean, accurate, uncluttered information. It becomes a single source of truth and a marketing-focused actionable intelligence resource.
But a CDP with data enrichment capabilities is even better. Value-added data enrichment activities include adding third-party data from multiple sources to existing data (providing more product information to the customer), correcting errors in the database, keeping information up to date, and performing other actions that refine and enhance data for high-impact marketing, enabling an organization to:
- Create new opportunities by reactivating cold leads in a database.
- Qualify and segment leads in real time for swift and accurate lead routing.
- Pinpoint target audiences on both the lead and account levels then personalize campaigns and activate them on their preferred channels to reach those prospects effectively.
- Maximize engagement and increase conversion rates.
Tipalti’s solution was look-alike modeling with deep learning AI. By automating their target account list building process, Tipalti was able to grow its market reach by 13%. Leads generated in this manner saw a 20% higher conversion rate than leads from traditional data providers.
It used to be difficult for marketing to demonstrate its value, and sales traditionally got the lion’s share of the credit when customers respond and purchase a product or service. By using hard data to target what customers want with pinpoint accuracy, marketing can now show the exact path to purchase that prospects traveled on -0 proving their value and why the marketing manager deserves that raise or promotion.