To succeed in today’s market, brands need to establish an integrated analytics framework that enables access to insights derived from wide variety of data points. Enabling a truly analytics-led marketing ecosystem helps brands to make more informed decisions at every step of the buyer’s journey and optimize return on marketing investment (MROI).
Some of the key benefits of marketing analytics include:
- Leveraging data as a strategic asset
- Gaining a full view of customers across channels
- Proactively and effectively making decisions
- Better understanding customer needs and drivers
- Quick access to insights to engage your customers at the right moment
- Data visualization to share and collaborate across the organization
But all the data and analytics in the world doesn't help your marketing team if you don't know how to apply the right models based on what you're trying to achieve.
Use this graphic as a starting point.
Let's break it down in a little more detail:
Here's why you might want to achieve the goals represented by the outer ring on the graphic.
- Attribution Modeling: To truly understand the buyer’s journey, marketers need to look beyond clicks. Attribution modeling allows brands to measure the merit of each channel and interaction for each individual to holistically understand the MROI. You’ll want to consider using Markov Chain Monte Carlo (MCMC).
- Pricing Analysis: Analyze and compare competitive pricing dynamics using optimization techniques to intelligently devise an efficient pricing strategy. Pricing analysis allows brands to track price changes, understand buying behavior and provide insights on pricing strategy.
- Media-Mix Modeling: Tie marketing investments to business results and quantify the contribution each channel makes at a high level using panel data regression.
- Segmentation: Make quicker and smarter decisions about your buyer’s needs and context through segmentation. Segmentation through cluster analysis helps brands to acquire actionable data to improve your interactions in a contextual manner.
- Forecasting: Predictive analytics with multivariate time series models mine and analyze current and/or historic data to make predictions about the future.
- Targeting: Target buyers with customized offers and incentives based on their lifetime value. By analyzing buyer’s behavior, engagement, lifestyle attributes and demographics with RFM (recency, frequency, monetary) analysis, marketers can increase the effectiveness of their marketing dollars.
- Churn Analysis: Predict in advance which customers are going to churn and use predictive modeling like survival analysis to know which marketing actions will help to stop the churn before it happens.
- Purchase Likelihood: Optimize marketing dollars with propensity models to understand if certain buyer behaviors indicate a higher likelihood to buy.
- Cross-Selling and Upselling: Apply analytics like basket analysis to recommend products that customers with similar needs have bought. Enable strategies tailored to different segment-level profiles to drive a truly customer-centric sales approach.
- Next Best Offer: Predictive analytics like collaborative filtering help marketers to better understand customers' needs and drivers to provide personalized next best actions—which make customers more likely to take the intended action and further their relationship with the brand.
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