Examining the four types of big data analytics

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Examining four types of big data analytics

In the first of a two-part series, Vertical Leap discussed how to prepare your data for analysis. Here, they explore how it can be applied to various types of analysis, helping users make the decision of which one to choose.

Predictive analytics is probably the most sought-after with an estimated market worth of $12.41 bn (£9.89 bn) by 2022. However, getting to the predictive or even prescriptive analytics stage is impossible without leveraging the two other pillar analytics types – descriptive and diagnostic analytics.

Each of them plays an important role and can be strategically employed to improve different aspects of your marketing.

1. Descriptive analytics

Before we learn where to go, we need to know where we came from. That's the key question descriptive analytics solutions tackle. By mining historical and real-time data, such tools identify repeating patterns and data correlations.

Google Analytics is a well-known example of descriptive analytics in action. While the platform offers a comprehensive overview of all sorts of SEO parameters – CTR, impressions, positions in search results etc. – it doesn't explicitly tell us why we are seeing these numbers.

Despite presenting us with bounded knowledge, descriptive tools still carry massive benefits for marketers:

  • Trend reporting: monitor the changes as they occur. Descriptive tools can be set up to report on multiple parameters – follower growth, customer attrition rates, average conversion rate per page and so on.
  • Prospect classification: James and Joanna are in their late 30s, both spend at least £55 with your brand every week; schedule deliveries on Fridays; and apply coupons to 15% of their purchases. A descriptive algorithm will match-make these two in one audience profile, along with hundreds of other lookalike prospects. Your leads or customer lists can be neatly organised this way to maximise conversions and personalisation.
  • Audience segmentation: similarly, you can distinguish between different types of prospects on your email list; following you on social media or just browsing your goods online.

2. Diagnostic analytics

Diagnostic analytics tools help you uncover the root cause of some problems. For instance, you have this normal looking, well-optimised website page that refuses to get ranked by search engines.

What do you do? Perhaps, you run a URL inspection in Google Search Console and receive a notification that this URL has indexing errors. Well, that’s diagnostic analytics in action.

The use case of diagnostic analytics in marketing can be broadly summed up in the following categories:

  • Uncover and explain anomalies. Descriptive analytics will pinpoint you towards unusual happenings – pages with lowering conversions or PPC campaigns driving abnormally high results. Diagnostic analytics provide an unbiased, data-driven explanation of causation and show the exact parameters you need to adjust for a positive change.
  • Debunk false interpretations. When working with the same data day-after-day, it’s easy to draw quick conclusions. You’ve seen that traffic dip before; there’s nothing unusual in that, so no point in investigating. However, such dismissals often lead to poor decision-making. By zeroing in on the wrong information or your personal bias, you let important data stories escape from your scope.

3. Predictive Analytics

Predictive analytics “joins the dots” between the accumulated and analysed data points, conveying what and why something happened, into models suggesting what can happen next. It indicates the probability of certain outcomes with high accuracy and takes the guesswork out of your decision-making process.

Marketers are increasingly on-board: 91% of industry leaders are either fully committed or already experimenting with predictive marketing. Here’s what most are after:

B2B lead scoring optimisation. Forrester identified three major segments where B2B companies are seeing the most successes with predictive analytics.

  • Predictive lead scoring:Obtain new insights that can improve the conversion rates of your campaigns. Algorithms can automatically qualify each new prospect entering your system and suggest the most profitable actions to take.
  • Identification models: Locate and acquire “lookalike” prospects suggested by an algorithm after churning all the available data.
  • Automated segmentation: Create ultra-niche audience segments to target with personalised messages and offers.

Data-driven conversion optimisation. Fact: personalised web experiences command higher conversions. Predictive analytics solutions can deliver relevant content/product suggestions to users and gently guide them through the entire purchase session. The average lift in conversions for such “influenced” sessions is 22.66%.

Additional use cases of predictive analytics also include customer lifetime value optimisation; advanced campaign targeting; focused content distribution and comprehensive SEO optimisation.

4. Prescriptive analytics

Prescriptive analytics is yet to move from the margins to the mainstream. It's an emerging area of analysis attempting to answer the complex question of "what actions to take if I want to get outcome A?"

Prescriptive tools come up with multiple future outcomes based on your current/past actions; match those futures with your goal and advise you on the action you need to apply. But, we are not quite there yet. Only some use cases of prescriptive analytics in marketing have proven to be successful. For instance, one Spanish bank has successfully deployed IBM prescriptive algorithms to filter the best new sales opportunities from their seven-figure list of customers. That data was applied to improve customer satisfaction with the provided services and resulted in higher ROI.

In 2019, we expect more prescriptive solutions to emerge in the MarTech universe, simplifying an array of complex decisions made by marketers every day.

George Karapalidis, head of data science, Vertical Leap

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