Requirement for such tools exists since firms struggle with how to turn enormous information into meaningful insights. According to some 2015 study”Obtaining Digital Right” by advertising consulting company Milward Brown Digital, just 14 percent of respondents were confident in their institution’s use of large data. Nevertheless, 67 percent of entrepreneurs use consumer behaviour as a research tool to affect marketing decisions.
Predictive-marketing analytics tools help businesses manipulate data to anticipate how shoppers will act in the future…
Selecting an expert to make sense of this data is expensive and experience is in short supply. Only large companies can manage an inner, customized approach. Several SaaS companies have provided an alternative, offering cloud-based predictive analytics solutions that eliminate the need to employ a data scientist.
Predictive marketing tools can get data collected from most third party customer-management applications, so no data source is missing. Furthermore, advances in machine learning allow automated marketing systems construct versions, deploy lead scores, and learn from outcomes in real time.
Following are a few of the advantages of working with these predictive marketing tools.
6 Reasons to Use Predictive Marketing Tools
Improves customer participation and raises revenue. Predictive marketing analytics can save businesses money by honing marketing campaigns and eliminating campaigns which don’t resonate with shoppers. Analytics can increase conversions with personalized, well-targeted advertising that turns shoppers into buyers. Predictive marketing analytics are especially beneficial in B2B sales, where customer acquisition is often more costly than in B2C. Predictive analytics can sharpen a business’s cross-sell, upsell, or renewal choices.
Customer lifecycle analytics (among those subsets of marketing analytics) can convert one-time buyers into repeat clients, building greater value from existing clients. Analyst firm Gartner says that it costs five times more to acquire a new customer than to keep an existing one; it is reasonable to cultivate loyal customers.
Helps smaller ecommerce merchants stay aggressive. Large ecommerce businesses like Amazon and Netflix utilize predictive marketing analytics and recommendation engines to provide customers suggestions for additional purchases. Personalized product recommendations can only be discovered using predictive analytics. Data scientists are now able to create machine-learning algorithms that offer real time, personalized offers for various customers. With the introduction of affordable SaaS solutions, smaller ecommerce businesses are now able to use such tools.
Facilitates more complex segmentation of information. The prevalence of using social media and location-based messaging for business means that firms have more sources of information on prospects and client preferences.
Putting all the resources together allows marketers to collect valuable insights. This translates into more complex segmentation, which then sharpens the advertising message. Because of this, campaigns are more effective in conversions, and funding and resources are concentrated on who in the market will buy.
By way of instance, Nordstrom’s Technology People Lab team decided to find customer insights from 1.2 billion online product views and 200 million transactions. Data sources included stock, merchandise metadata, email campaigns, click streams, and purchase information. Clients were scored for their interaction with product sections — viewing, adding to mill, and buying. Nordstrom developed an”inferred scoring” system that raised email-to-dollar spent conversion by 25 percent. The group also developed a model that predicted the prevalence of repeat clients with an overall precision of 76 percent.
Using predictive marketing analytics tools, merchant Nordstrom developed an”inferred scoring” system that increased purchases from email by 25 percent.
Identifies promotions which are better targeted to clients. Algorithms can forecast a shopper’s answer to a marketing communication, the impact on customer behaviour, and any incremental effect of multiple messages. These algorithms make it possible for marketers to pick the best campaign message for each individual and determine the best mix of marketing communications.
Minimizes wasted advertising dollars. Understanding what techniques and what marketing channels work best for every customer lets companies carefully aim communications instead of randomly bombarding prospects. With predictive analytics, marketers can determine where to advertise and how to enhance email and direct mail campaigns. This leads to fewer wasted advertising dollars.
Improves lead scoring. For B2B marketers, better direct scoring is most likely the most obvious use of predictive analytics. Lead grading is a strategy that ranks prospects on a scale which reflects the perceived value of each lead into the organization.
To ascertain lead scores, a provider gathers information about prospects to gauge their probability of taking a desirable action — usually purchase intent. Other dimensions include customer lifetime earnings, profitability, and advertising response. Sales departments use scoring to assign priorities to prospects for sales contact and follow up. Model-based scoring has turned out to be more precise than manual formulas.
Predictive Marketing Analytics SaaS Providers
B2C ecommerce marketing analytics SaaS providers comprise these companies.
- Retention Science
B2B SaaS providers incorporate these companies.
- Lattice Engines
Marketing analytics tools will become more successful in the coming years as machine learning improves. Both B2C and B2B merchants can likely gain a competitive edge in this early adoption stage with these services.