None of us would agree to play a card game with cards missing from the deck; we would know that the odds of winning would be significantly diminished. Yet surprisingly, many marketers are willing to implement marketing programs sans analytics.
In the past few weeks I have attended several marketing conferences. At each event, marketers are talking enthusiastically about how to make Web sites, SEO, social media, email campaigns, and mobile better. However, there is very little conversation about how to be smarter. Analytics is an essential card — actually an ace — in every marketer’s deck for enabling fact-based decisions and improving performance, and most importantly, for being smarter.
While the ace alone has value, when played with other cards its power is truly revealed. And when it comes to analytics, the other card is data. Yes — we have all heard the common complaint about the elusiveness of quality data. Unfortunately, data quality has been an issue in organizations for so long that it has now become the ready excuse for why marketers cannot perform analytics. To harness the power of your analytics card, identify your data issues and create a plan to address them.
Another reason that you may overlook this missing card in your deck is that guessing or gut instinct has been working well enough. Unfortunately, this approach may not suffice in the long-term and your “luck” may run out as organizations push to make “smart” decisions. As marketers, analytics is our opportunity to actively contribute to fact-based decisions. Through analytics, marketers achieve new insights about customers, markets, products, channels, and marketing strategy, programs and mix. It also enables marketing to help improve performance, competitiveness, and market and revenue growth.
As the importance of analytics gains momentum, marketers with analytical acumen will be in great demand. According to some resources, the complexities of data analysis and management are becoming so enormous that there is a shortage of people who are able to conduct analysis and present the results as actionable information. Taking the initiative and honing your analytical capabilities will enable you to make sure you have this ace in the deck — and preferably, in your hand.
Most of us are already working with a time and resource deficit. Try to find a way each quarter to bolster you analytical skills. Attend a conference, read a book, take a class, and bring in experts you can learn from. Here are some key analytical concepts and skills to add:
· Quantitative Decision Analysis
· Data Management
· Data Modeling
· Industry and Competitive Analysis
· Statistical Analysis
· Predictive Analytics and Models
· Marketing Measurement and Dashboard
If you can build your analytics strength, you’ll always have an ace in your pocket.
For businesses, a pipeline is a targeted list of potential buyers who might have an interest in your products or services. Many companies face the challenge of capturing the attention of potential buyers and moving as many of these potential buyers as possible through the pipeline stages of contact, connection, conversation, consideration, consumption, and community. More and more companies are relying on Marketing to continuously and effectively grow their organization’s opportunity pipeline. Potential buyers who are not converted into customers are often referred to as leaks or pipeline leakage. Our role, as marketers, is to “plug the leak” and improve conversion rates. If the Marketing and Sales aspects of the pipeline are not connected and aligned properly, the potential pipeline leakage can be very large. So a crucial step is ensuring Marketing is properly aligned with Sales. Marketing and Sales alignment allows for the creation and implementation of strategies, programs, and tactics that will facilitate pipeline opportunity development and movement. Once your company achieves this alignment, the next important step is for Marketing to focus on marketing initiative that will effectively and efficiently contribute to pipeline performance and the generation of customers. We must be able to clearly demonstrate and measure our contribution to the pipeline.
Unfortunately, a Forrester Research study, “Redefining B2B Marketing Measurement,” found that “the metrics that most B2B marketers say they use — like number of leads generated and cost per lead” — rank in the lower half of the effectiveness list.” In fact, number of leads generated and cost per lead may actually work against us if we don’t look further into the buying process. At first blush, one program may produce more “leads” than another at a lower cost and therefore appear more efficient. But what is really important is how many of the opportunities convert (don’t leak) to the next stage in the buying process. If there is a higher conversion rate from the more expensive program, than it is actually more effective. If we only look at a marketing program in terms of qualified leads generated and cost, we could potentially be eliminating programs that actually help build the pipeline.
Therefore, we need to move beyond the lead as the marketing metric and leverage metrics more meaningful to the organization — metrics that are more closely tied to customer deals. Customer deals– that is, sales — is for most organizations one of the most important business outcomes. Every company establishes a revenue goal. This revenue target is generated by some number of deals and dollars from existing customers and some number of deals and dollars from net new customers. This brings up the question of what metrics should CMOs and their teams use to measure Marketing’s contribution to the pipeline? Here are four metrics to consider:
1. Pipeline contribution which measures the number of opportunities generated by Marketing that convert into sales opportunities and ultimately into new deals. This metric helps ascertain to what extent marketing programs and investments are positively effecting the win rate and reducing the number of qualified leads that wither and die or are rejected by Sales.
2. Pipeline movement which measures the rate at which opportunities move through the pipeline and convert to wins. This metric helps assess the degree to which marketing programs and investments accelerate the sales cycle.
3. Pipeline value which measures the aggregate value of all active marketing opportunities at each stage within the pipeline. This helps determine what increase in potential business marketing investments may generate.
4. Pipeline velocity which measures the rate of change within your pipeline-both in speed and direction. This enables you to determine whether your sales are accelerating, decelerating, or remaining constant.
When examining each of these metrics it is important to compare the marketing generated opportunities compared to non-marketing generated opportunities. This means we need to understand what is the difference in the win rate, average order value, conversion rate, and velocity between marketing generated opportunities compared to non-marketing generated opportunities. Ideally, over time, by monitoring results and analyzing the data related to these metrics, Marketing can begin to create more predictable results in terms of contribution, conversion, and value.
Recently there’s been plenty of focus on predictive analytics. We were recently privileged to create a Take 10 webcast on this subject for MarketingProfs . Why all the interest? Companies want to be able to apply a variety of statistical techniques from modeling, machine learning, data mining and game theory. This way they can uncover relationships and patterns in order to predict behavior and events, such as attrition, propensity to purchase, incremental lift to maximize impact and optimize marketing mix and spend.
These models assign scores or ranks to each customer based on probabilities in order to predict a single behavior, such as which customers are most likely to buy a specific product(propensity to purchase modeling), which customers are most likely to be influenced by a specific promotion (response modeling), or to calculate customer lifetime value.
As with any model development, you will need to perform the usual data cleansing, transformation, initial and ongoing validation and refinement. These steps will help you begin creating a propensity model.
First, you need a suitable modeling sample. This requires enough records (thousands) that are recent enough to be relevant so that you can simulate various scenarios and perform the appropriate analyses. Odds are you will be using a variety of internal data sources, such as transaction, contact, weblog, text, and campaign data as well as appending external data to improve the quality of your model. The more instances of what it is you are trying to predict the more robust a model you can create.
Second, once you have your sample, check it carefully for biases.
Third, establish your criteria and ranks based on weighted attributes and build the model.
Fourth, similar to testing a new pharmaceutical, test your model with both a treatment and a control group. It will be essential to have clean control groups so that comparisons are truly actionable. This allows you to find the buyers, responders, etc. in both groups and also ascertain what kind of people did not perform the desired behavior in the control group but did so in the treatment group. These are the customers whose behavior was impacted only because of the treatment. You can now build a propensity model.
In parting, once you create and implement the model it will be important to communicate the results and the value generated as a result of the model.