The 7 Leverage Points for Using Data to Make Marketing Better
Data, big data, cloud computing, The Internet of Things: data and network-related terms remain the key business buzzwords of this decade. However, buzzwords do not always translate clearly to our everyday lives.
For example, when it comes to using data to make our marketing efforts work better, we find that most business owners have a vague sense of the benefits of doing so. However, most lack a clear mental picture of just HOW data can be applied to improve marketing in a nuts-and-bolts kind of way. They require some specific examples in order to set them on the right path.
So, where exactly do savvy marketers, business intelligence gurus and data geeks apply their data skills to marketing? Here are the 7 leverage points for using data to make marketing better:
1. Store and organize contacts and transaction information: The most basic – but by no means least important – aspect of applying data to marketing is that of managing on-hand information about customers, products & services and transactions. The domains of application in marketing include the use of customer databases, prospect databases, purchase transaction databases, product SKU databases, and CRM systems.
2. Allocate resources & budget: Budgeting and resource allocation should be the cornerstone of any annual marketing planning effort. This simply involves aligning each initiative in your marketing plan to a specific expenditure (i.e., dollar amount) over the time period in question. Key considerations are demand seasonality, cash flow, and available marketing expertise. The domain of application in this case is usually an Excel spreadsheet used to plan for the coming fiscal quarter or year.
3. Understand, rank and prioritize your customers and prospects: An extremely valuable exercise is to identify your best customers and determine the salient attributes (i.e., characteristics and behaviors, as found in acquired or collected data) about them that set them apart from the rest. By grouping all of your current customers by these data-oriented attributes, you can then apply this model to finding more prospects just like them. This is the application domain of market segmentation, focus groups, surveys, lead scoring and prospect list prioritization.
4. Expand and refine your targeting options: Once you know who your best segments are, you will need to organize your prospects in terms of your best customer model. This can involve leveraging on-hand prospect data and/or sourcing new lists, then applying the proper filters in order to focus resources on the high-payoff segments. This is the domain of database filtering, list sourcing, and customer targeting.
5. Tailor your messaging and creative to the right target audience: The results of your data-driven segmentation, ranking and targeting efforts should also inform your choices when building new creative (i.e., logos, images, banners, formatting, fonts and layout) or when writing content. There are myriad ways to deliver your brand’s positioning, value proposition and call to action via the written word. Similarly, there are an almost infinite number of ways to build your creative pieces. This is the domain of content creation and creative development.
6. Establish metrics, act, analyze, optimize: In any marketing campaign, the flow of action should follow the traditional “Plan, Do, Check, Act” cycle. The central idea here is to establish metrics (i.e., factors or aspects of your campaign that can be measured and evaluated quantitatively) and set initial targets for each metric before executing the marketing action. Next, take the marketing action itself. Then, collect the data that corresponds to the pre-established metrics to find out whether you exceeded, met, or did not meet the stated targets for each metric. Finally, act on those results and adjust the action as needed. (This is akin to the feedback loop in system dynamics).
The relevant marketing domains in this area include, for example: adjusting bids on Ads campaigns, altering content strategy for SEO, nurturing leads with follow-up e-mails based on their response to an initial eblast, revising media buying decisions, adjusting direct mail reach or frequency, and reporting campaign results internally or to clients.
7. Predict future behaviors: This is where things get a bit more complex but also really interesting. Predictive modeling is the science (and art) of predicting future outcomes based upon past behaviors or phenomena. While the past never exactly equals the future, it does provide a strong indicator of where things are headed based upon a given situation (i.e., given independent variables). Techniques used in this area include statistical methods like Naive Bayes and k-nearest neighbor algorithms, as well as machine learning techniques such as artificial neural networks. Application domains in marketing include cross-sell/upsell analysis, product mix analysis, site selection, and customer behavior modeling.
Most or all applications of data to making marketing better can fit into this framework. We always appreciate your feedback and input. Contact MindEcology with questions or comments.