With predictive analytics becoming mainstream in the 2013-2018 period, it’s being increasingly used by organizations from marketing and sales to finance and operations to better achieve business performance.
- Predictive analytics is forwards looking
- It complements Business Intelligence
- It’s being used to humanize customer experience
- Predicting highest churn probability customers
- Predicting likely highest customer lifetime value clients
- Predicting which products best match customer preferences via real-time behavioural, web and social data
Hybrid marketing teams, with components of automated marketing platforms and human intelligence perform the best and have the greatest ROI.
Predictive analytics allow for sophisticated machine-learning data mining via algorithms used on both structured and unstructured data to determine outcomes. 2013-15 period was the turning point in this technology due to:
- Increased computing power
- Improved machine-learning processes
- Automation and personalized become more salient in Marketing
- Economic benefits, value of insights, competitive pressure
- Value their customers and want to predict their actions
- Privacy of consumer data no longer the issue it once was
- Millennials (born after 1980) driving Customer experience to the next omni-channel paradigm
- Mobile usage drives easier tracking of consumer behaviour
- Ease of use, the machine-learning can do the “work” for you
With Big Data collection, the idea that analytics can give a competitive advantage became mainstream. Predictive analytics algorithms becomes the natural heir and star-child of this phenomena.
BI vs. PA
- PA allows for estimating of target outcomes, probability and to anticipate human behavior to boost sales outcomes
- BI is good at slicing and dicing data into dashboards of what happendor why this is happening
- Both are necessary parts of the corporate and marketing fundamentals
Why do Companies Adopt Predictive Analytics?
- The ability to understand and predict trends and behaviours
- Understanding customers and their preferences and tracking their actions real-time with triggered CTAs.
- Retention analysis and improving customer loyalty
- To drive better business performance
- Give insights into strategic decision making
- Add to operational efficiency
As adoption of this technology grows, education and insight into its efficacy does as well. More case studies will begin to emerge and Marketing automation platforms that integrate it will be coveted. This is also because our entire society is moving towards a paradigm of smart automation. From Internet of Things to 3D printing, to more intelligent AI agents, you can read wild assessments of what % of current jobs will be automated by 2030, and it’s considerable number.
With the Big data revolution, for enterprise business, analytics is becoming a significant part in decision making. Companies that do not use analytics to drive decision making are late adopters and may be at a disadvantage.
Predictive Analytics in Marketing
The following areas are most often cited as indication of the best current utilization of predictive analytics:
- Direct Marketing
- Cross-sell – Propensity to spend – Upselling
- Retention analysis
- Fraud detection
- Quality assurance
- Portfolio analysis – prediction
- Risk analysis
- Improved customer experience – personalization – advanced segmentation
The majority of early adopts in 2014 were Marketing & Sales teams, and this is not surprising.
Most likely users and most active department where it’s use is implemented:
- Marketing Department
- Executive management
- Customer service support
- IT, networking, computer management
- R & D, Engineering and scientific research
- Online presence & social media
- Productive development & life cycle management
So if you work in Marketing, Sales or Management. Maybe it’s time you look at your options regarding this.
1 – Structured data (tables & records)
2 – Time series data
3 – Demographic data
4 – Web log data
5 – Clickstream data from websites
6 – Real-time event data
7 – Geospatial data
8 – Internal text data (emails, call center notes, claims)
9 – External social media text data
10 – Machine-generated data (RFID, sensor data)
Barriers to Adoption
- Lack of education or skillset; lack of staffing fit
- Lack of skilled personnel
- Inability to assemble necessary data
- Lack of budget
- Lase of case studies to warrant the risk
- Technology is too hard to use
- Insufficient computing infrastructure
Who typically builds the Models with Predictive Analytics in Companies
- Data scientists or Statistician
- Business analyst
- External partner
- IT developer
- Other corporate user
Key Skills to help Integrate PA
- Knowledge of your Business industry
- Critical thinking
- Knowledge of source data and model development
- Training in predictive analytics
- Degree in math, statistics or quantitative area
- Communication skills
- Training on the software
I hope you found this information helpful. Does your company have any experience with predictive analytics?
Here at Intema, we have our own patented software that operates as a hybrid human-AI mobile and ecommerce predictive analytics SaaS solution. It’s a turnkey omni-channel optimizing software to streamline the customer experience with APIs that can plug into your system effortlessly.