E-commerce business needs to collect data from various sources, analyse them in real-time and gain insights to understand the visitors behaviour, patterns which will allow the company to serve the customers in contextual and better ways to boost the conversion rate. A real time data platform is need of the hour which can combine stream analytics with Graph and AI to enable predictive analysis for better personalisation for the users which significantly improves the sales by 2X or even more.
Real-time and predictive data-platform for boosting e-commerce sales by visitor analysis is need of the hour
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Some of the general facts (statistical) which relates to e-commerce sales are following
- Based on survey reports, 45% of online shoppers are more likely to shop on a site that offers personalized content/recommendations
- According to a report by Gartner, personalization helps in increasing the profits by 15%
- Majority of the data (more than 60%) is not even captured and analyzed for visitor or customer analytics
- Less than 20% data is captured in true real time, which diminishes the potential of relevant and contextual personalized engagement with the visitors and hence lead scoring as well
To boost sales, e-commerce is looking to answer some of the following questions in general
- How to develop personalized real time engagement and messages, content
- How to engage with the visitors and customers in 1-on-1 basis for higher CR
- How to identify and leverage purchasing patterns
- What the entire consumer cycle looks like
- Ways to improve promotional initiatives
- How to make the customer and the customer experience the focus of marketing strategies – better lead score and reasons for the score
- How to identify your customers’ switches between each channel and connect their movements and interactions
The businesses typically seek to predict following for predictive analysis
- Personalized content in 1-0n-1 manner for better next steps or conversion
- Exactly which customers are likely to return, their LTVs
- After what length of time they are likely to make the purchase
- What other products these customers may also buy at that time
- How often they will make repeat purchases of those refills
What are some common challenges e-commerce businesses face?
- Understanding who is “Visitor” and “Potential Buyer”
- Relationships between different entities and the context
- Nurturing the existing prospects
- Calculating the Lifetime Value
- Understanding the buyers’ behavior
- Cart Abandonment
- Customer Churn
So, how can e-commerce businesses tackle the above challenges?
Predictive Analytics encloses a variety of techniques from data mining, predictive modelling and machine learning to analyze current and historical data and make predictions about future events.
With Predictive analytics, e-commerce businesses can do following
- Improve Lead scoring
- Increase Customer retention rates
- Provide personalized campaigns for each customer
- Accurately predict and increase CLV
- Utilize Behavioral Analytics to analyze buyers’ behavior
- Reduce cart abandonment rates
- Use Pattern recognition to take actions that prevent Customer Churn
Following are brief list of use case that can be enabled on BangDB
A. Real time visitor scoring for personalization and lead generation for higher conversion
- Predictive real time visitor behavior analysis and scoring for personalized offering / targeting for much improved conversion rate. The potential increase in CR or expected biz impact could be 2X or more if implemented and scaled well
- Faster, contextual and more relevant lead generation for higher conversion
- Personalized content, offerings, pricings, for visitors in 1 on 1 basis, leads to much deeper engagement and higher conversion
- Projecting much relevant and usable LTV for every single user/visitor that could lead to better decision making for personalization or targeting or offering
- Inventory prediction for different product/versions/offering for better operation optimization
B. Improve engagement
- Personalized interaction and engagement with the customers
- Shopper’s Next Best Action
- Recommendations about relevant products based on shopping and viewing behavior
- Tailored website experience
C. Better target promotions
Collate data from other sources (demographics, market size, response rates, geography etc.) and past campaigns to assess the potential success of next campaign. Throw right campaign to right users
D. Optimized pricing
Predictive pricing analytics looks a historical product pricing, customer interest, competitor pricing, inventory and margin targets to deliver optimal prices in real-time that deliver maximum profits. In Amazon’s marketplace, for example, sellers who use algorithmic pricing benefit from better visibility, sales and customer feedback.
E. Predictive inventory management
Being overstocked and out of stock has forever been a problem for retailers but predictive analytics allows for smarter inventory management. Sophisticated solutions can take into account existing promotions, markdowns and allocation between multiple stores to deliver accurate forecasts about demand and allow retailers to allocate the right products to the right place and allocate funds to the most desirable products with the greatest potential for profit.
F. Prompt interactive shopping
Interactive shopping aims for customer loyalty. Integration of an online logistics platform helps maintain end-to-end visibility of purchases and orders, and business intelligence software helps process customer transaction data. It also enables retailers to offer multiple delivery options and can prompt customers for additional purchases based on their buying patterns. Consistent customer service, coupled with technology, can greatly increase customer reliability.
Data mining software enables businesses to be more proactive and make knowledge-driven decisions by harnessing automated and prospective data. It helps retailers understand the needs and interests of their customers in real time. Further, it identifies customer keywords, which can be analyzed to identify potential areas of investment and cost-cutting.
Challenges and Gaps in the market
- Need to capture all kinds of data, across multiple channels, not just limited set of data
- Need to capture all data truly in seamless and real time manner
- Store different entities and their relationships in graph structure and allow rich queries
- Need to auto refresh and retrain scoring model for relevant and higher efficacy
- Need to scale for high speed, high volume of data across multiple levels/ channels
- Need to have full control over the deployment and data
- Need to have the ability to add and extend the solution in easy and speedy manner in different context or domains as required
Gaps with the existing systems in the market
- Majority of systems (GA, Omniture etc.) are able to ingest limited set of data. It’s virtually impossible to ingest other related data into the system for better scoring model. Also, with these systems, it’s difficult to extend the ingestion mechanism for custom set of data, coming from totally different data sources than just the clickstream. Therefore, there is a need for a system which can ingests heterogenous custom data along with typical CS data for better results and higher efficiency
- Most of the systems ingests data with latency not acceptable from the solution perspective. For ex; GA allows limited set of data ingestion in real time, majority of data come with high latency. Omniture also has latency which is not acceptable to certain scenarios for the use cases. Therefore, there is a need for true real time data ingestion and processing system/platform
- All the systems come with pre-loaded model(s) which are trained outside the actual processing system. This is hugely limiting from the AutoML perspective where the models could be trained and improved as it ingests more and more data. Also, finding the efficacy of the model is limiting which may result to poor and non-relevant prediction. Therefore, there is a need to have AI system natively integrated with the analytic scoring platform
- As we wish to deploy the system for various locales, different verticals, websites or companies, it is imperative that the system scales well. The speed and volume of data coupled with model preparation, training, deployment etc. make it very difficult for such system to scale well. It takes many weeks and months just to prepare and integrate the system with the new set of data sources. Software deployments, library configurations, infrastructure provisioning, training and testing of models, versioning of models and other large files, all of these creates huge block in terms of scaling the system. Therefore, there is a need to have a platform which hides all these complexities and provides a simple mechanism to scale linearly for higher volume of data, or more num of websites, locales or simply for larger number of use cases as things move forward.
- Most of the system acts as black box allowing lesser control on deployment and access to data in larger sense. This results to brittle solutioning and faster development of use cases. Better access to
- Most of the systems in the market won’t have “stream”, “graph”, “ML” and “NoSQL” at the same place. Integration takes lots of time, resources and sometime not feasible at all
- Also, it provides huge restrictions in terms of dealing with ML since the models and their meta data are often abstracted. More often than not, we might need to upload pre-baked models or model creation logic or file to leverage existing code. Therefore, we need a system which allows us to have greater control of various processes along with ability to reuse and extent already existing knowledge and artifacts
BangDB platform is designed and developed to address the above gaps and challenges.
- Captures all kinds of data for visitors
- Click stream, pixel data, tags, etc.
- Website specific data
- Any other data that may be useful/required
- Existing data
- Retailers’ data, external data
- Any other infrastructure or system data as required
Captures all data in real time
Captures all data in real time, as opposed to GS which captures only small fraction of data in real time. This limits the scoring efficacy as real time data is basis for proper analysis. Omniture captures most of the data, but they are available for analysis in few minutes rather in few milli seconds. Proper personalization or any action is best taken as soon as possible, not after few minutes or hours
Accelerated time to market
BangDB comes with a platform along with a ready solution which implement the use cases as needed and have majority of the plumbing in place. Further, it has the built in KPIs, models, actions, visualizations etc. which are ready from day 1. We need to just configure the system, add more data points, fix the API hooks etc., set the model training / retraining processes which is in contrast with many other systems where they may take several weeks or even months to just get started
Scales well across multiple dimensions, in simple manner
Several IPs for high performance and cost-effective method to deal with high volume of fast-moving data. The platform has built in IO layer for improved, flexible and faster data processing. Convergence allows system to scale linearly as required in uninterrupted manner
- Integrated streaming system to ingest all kinds of data as required for analysis in real time. Build your own apps/solutions or extend the existing ones as needed with just using the UI and not doing coding etc.
- Integrated machine learning system for faster, simpler and automated model training, testing, versioning and deployment
The platform comes with AI natively integrated, which allows us to get the models be trained and retrained on frequent basis as more and more data arrives. It starts producing output from the model with a week’s time and as it moves forward it keeps improving the model and its efficacy. It also measures its efficacy and tunes/retunes as needed for higher performance.
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