Smarter marketing requires the incorporation and tight integration of business insights with business intuition. The firms showing greatest success on this front are those that have infused data as part of their DNA across all facets of their operations. Whether this data is sales data or data documenting the process by which consumers interact with the brand, a data-driven organization can uncover hidden nuggets of information and interactions that may not have otherwise manifested themselves.
The most successful organizations integrate data analytics with the aggregated business acumen of their marketing executives to drive strategic marketing decisions. In many cases, business intuition cultivated through years of industry experience can not only support better decisions, but also inform hypotheses regarding consumer behavior that can then be tested in a data-driven environment.
The proliferation of channels used by today's customers (and marketers) adds additional complexity. Consumers engage with a brand, and with other customers, detractors, influencers, and sometimes even competitors, well before they make a purchase. Marketers must adapt and adopt new approaches to decipher these diverse forms of customer engagement. One approach includes incorporating predictive analytics into the brand's multichannel data analysis to gain deeper insights regarding consumer behavior and motivations.
Most data-driven organizations consider predictive analytics to be one of the most valuable tools in their marketing arsenal. By applying advanced mathematical techniques, marketers can uncover patterns in data that may not be evident otherwise. These statistical techniques, many of which are drawn from other disciplines (e.g., survival analysis from clinical drug trials), are being applied to better predict and understand business and customer behavior. Moreover, these techniques are helping organizations validate or negate "old school" business intuition (gut feelings) with some degree of confidence supported by statistical significance.
Multichannel customer data can include various types of structured and unstructured components captured across online (Web logs, Web site and session data, mobile), social (Facebook, Twitter, and voice of the customer), and offline (customer support, telesales, brick and mortar) channels. The data collected from these channels is typically high-volume and, in many cases, does not easily integrate with data from other channels. However, with careful planning and investment in the appropriate technologies and data structures, data-driven organizations can harness the power of this complex, unwieldy multichannel data. Further, with the application of predictive analytics techniques, organizations can uncover behavioral patterns that may have otherwise been overlooked or, even worse, never found.
Predictive analytics helps marketers address key components of multichannel customer engagement in order to connect marketing efforts with return on investment (ROI). These components include:
1. Brand interaction: The organization can make better marketing decisions by understanding the influencing nature of each channel (a prospective customer who engages with other purchasers on social media may be more willing to make a purchase), the interactions between channels (does an online visit influence an offline purchase?), and the overall buying sequence. Since a large number of prospects visit social media properties to ask questions regarding products, an organization that provides the appropriate links to product feature Web pages can help customers gain information quickly.
2. Revenue attribution: By recognizing the role of each channel in the purchasing cycle of a customer, the organization is able to better link and attribute revenues to channels and/or teams responsible for servicing the customer during the sales cycle.
3. Understanding customer value: Through the use of finance techniques alongside predictive analytics, marketers can derive and quantify the total monetary value of customers over their entire span of interaction with the organization.
Effective Marketing Investments
Limited marketing budgets and constant pressure to quantify marketing's ROI call for innovative ways to allocate marketing spend. Predictive analytics allows marketers to use customer buying patterns to better allocate marketing resources in an effort to maximize revenues. Marketers can target prospects (and in the case of upselling opportunities, customers) who have the highest probability to purchase. This allows marketers to reduce their marketing costs (steering away from the "spray and pray" method) and lower the risk of alienating their customers (potentially reducing customer churn), while maximizing potential revenue. Moreover, predictive analytics allows marketers to test various hypotheses regarding marketing offers, collateral, and channels and also understand which factors may be predictors of a future purchase.
Through Analytics as a Service-type models and partnering with experienced vendors, many marketing organizations can quickly equip their marketers with high-end advanced analytics capabilities without the cost of hiring and managing these resources. The advances in cloud computing, networking, and data security have significantly lowered the barrier to entry for organizations seeking predictive analytics.
Given the abundance of customer data now available, organizations must make the collection and analysis of customer data an integral part of their strategy and DNA. Moreover, to really drive smarter marketing, these organizations must incorporate multichannel predictive analytics into their customer engagement strategy. Advanced analytics is no longer an option for marketers, but rather a requirement.
Shamez S. Dharamsi is global head for CRM, social media, and business insights in the sales and marketing services division at eClerx, responsible for the global CRM and business insights practice. He has over 15 years of experience with globally situated Fortune 500 organizations in the areas of marketing strategy, consumer behavior, and customer analytics.