Marketers regularly come across a wide range of data, both structured and unstructured, that can be utilized to plan and execute successful marketing campaigns. Artificial intelligence (AI) and Predictive Analytics takes campaign success to another level by introducing accuracy in planning, execution, and evaluation stages of a marketing campaign.
Even today, many small to medium organizations are storing data in Google Sheets or Excel files; and hiring resources who work round the clock to make sense of all that data in order to pass on comprehensible narratives to the decision-makers. This archaic way of collecting data and reporting is error-prone and not to mention time-consuming.
Website, social media, and digital ads analytics are readily available in structured formats that provide marketers with enough data on the effectiveness of campaigns on the target customer segment. Predictive Analytics can ingest, organize, and present the data from all marketing sources/channels into comprehensible and actionable insights.
Also, there is a huge volume of unstructured data online in the form of customer feedback, social media comments, and reviews that go completely unutilized in laying the groundwork for the upcoming marketing campaigns. Artificial Intelligence can make sense of all that unstructured data and turn those into quantifiable data sets that can be further utilized to make marketing decisions.
Now let’s take a look at how Predictive Analytics optimizes all the three stages of a marketing campaign
A. Planning Stage
- Customer Behavior- At the planning stage of a Marketing Campaign, marketers can rely on Cluster Models to accurately segment the target market based-on historical customer data. The clustering of customers based on variables such as age, gender, geography, frequency of purchases, the preferred channel of sales, and purchased product/service type, is quite common. Based on the type of business, the clusters can be even more granular. Whereas, Propensity Models can help in predicting the likelihood of customer engagement, conversion, or churn. Using both Cluster and Propensity models, accurate segmentation of customers/prospects can be identified which will have the highest propensity of engagement and conversion.
- Product / Services- At this stage, marketers also need to understand which product or offer they should be promoting in a marketing campaign. With the Data Visualization tool, they will be able to evaluate which product categories, types, price points, and product sizes that are best to promote for each customer segment.
- Marketing Channel- Using the cost per lead data from Digital, Print, Television, and Radio Ads, analysis of the most effective yet economical promotional platform can be selected. Through Predictive Analysis, the effectiveness of future campaigns on each marketing channel can also be predicted by factoring in audience engagement data for each channel.
B. Execution Stage
- Personalization- Personalized messaging for each customer segment is vital. Predictive Analytics insights can guide marketers to create relevant content for each customer segment. Predictive Analytics can also provide insights into the best performing pieces of content from pre-existing data to drive decision-making and content creation for upcoming campaigns.
- Prioritization and Timing- While executing a campaign, prioritization among all customer segments can be set up. For example, when campaigns are automated using AI and Predictive Analytics, it might factor in customer behavior based on timing and geography. It will not trigger an ad campaign for air conditioners during winter in tropical or equatorial countries, i.e. if that customer segment is least likely to respond to that messaging at the time.
- Cross-sell/Up-sell- Using collaborative filtering guided by Predictive Analytics, recommendations for other products/services can be generated for customers who have interacted or responded to a campaign based on their historical buying patterns and purchase history. Marketing campaigns that factor in up-selling and cross-selling opportunities perform exponentially well in terms of ROI.
C. Evaluation Stage
- ROI and Feedback- Statistical analysis based on regression techniques such as Marketing Mix Modeling (MMM) can provide an accurate evaluation of Marketing ROI by analyzing and cross-referencing campaign performance for each customer segment. Not just that, through analysis of end-user commentary online, AI will be able to produce data sets on future acceptability of such campaigns. It will be able to automate the creation of business insights for decision-makers to plan future campaigns with pre-predicted ROI.
- Best Practices- Applying Econometrics & Statistical Modeling techniques such as Attribution Modeling, Marketing Mix Modeling, Regression analysis, etc. to quantify marketing efforts, it is possible to measure almost accurately and identify the best practices in a company’s marketing strategy. It will also be able to precisely identify the marketing channels, messaging, and offering that is not yielding results in marketing campaigns.
With Predictive Analytics, Marketers are empowered to plan, execute, and evaluate marketing campaigns with supreme accuracy. This kind of precision in sending the right messaging to the right customer/prospect with a higher probability of conversion through the right medium will cut costs involved in the trial-and-error method of marketing. It will also increase the lifetime value and retention rate of the customers. Artificial Intelligence, Natural Language Processing, and Natural Language Generation can transform a large volume of business insights into easily consumable narratives for all stakeholders. Factor in all of these benefits and you have a game-changing strategy for your marketing campaigns. Reach out to the team of Data Scientists at Enquete Group to take a look at our Marketing ROI offering!