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2021 Blog, Blog, Featured, Spectra Blog

Oracle E-Business Suite (EBS) is an invaluable support to many businesses for managing their transaction data. However, business users would be familiar with limitations when it comes to generating even moderately complex analyses and reports involving a large volume of data. In a Big Data-driven world, this can become a major competitive disadvantage. Relevance Lab has designed SPECTRA, a Hadoop-based platform, that makes Oracle EBS reporting simple, quick, and economical even when working with billions of transaction records.

Challenges with Oracle EBS Reporting
Oracle EBS users often struggle to extract reports from large transactional databases. Key issues include:


  • Inability to handle large volumes of data to generate accurate reports within reasonable timeframes.
  • Extracting integrated data from different modules of the ERP is not easy. It requires manual effort for synthesizing fragmented reports, which makes the process time-consuming, costly, and error-prone. Similar problems arise when trying to combine data from the ERP with that from other sources.
  • The reports are static, not permitting a drill down on the underlying drivers of reported information.
  • There are limited self-service operations, and business users have to rely heavily on the IT department for building new reports. It is not uncommon for weeks and months to pass between the first report request and the availability of the report.

Moreover, Oracle has stopped supporting its reporting tool Discoverer from 2017, creating additional challenges for users that continue to rely on it.

How RL SPECTRA can Help
Relevance Lab recognizes the value to its clients of generating near real-time dynamic insights from large, ever-growing data volumes at reasonable costs. With that in mind, we have developed an Enterprise Data Lake (EDL) platform, SPECTRA, that automates the process of ingesting and processing huge volumes of data from the Oracle Cloud.

This Hadoop-based solution has advantages over traditional data warehouses and ETL solutions due to its:


  • superior performance through parallel processing capability and robustness when dealing with large volumes of data,
  • rich set of components like Spark, AI/ML libraries to derive insights from big data,
  • a high degree of scalability,
  • cost-effectiveness, and
  • ability to handle semi-structured and unstructured data.

After the initial data ingestion into the EDL, incremental data ingestion uses delta refresh logic to minimize the time and computing resources spent on ingestion.

SPECTRA provides users access to raw data (based on authorization) empowering them to understand and analyze data as per their requirement. It enables users to filter, sort, search & download up to 6 million records at one go. The platform is also capable of visualizing data in charts, apart from being compatible with standard dashboard tools.

This offering combines our deep Oracle and Hadoop expertise with extensive experience across industries.
With this solution, we have helped companies generate critical business reports from massive volumes of underlying data delivering substantial improvement in extraction and processing time, quality, and cost-effectiveness.


Use Case: Productivity Enhancement through Optimised Reporting for a Publishing Major
A global publishing major that had recently deployed Oracle Fusion Cloud applications for inventory, supply chain, and financial management discovered that these were inadequate to meet its complex reporting and analytical requirements.


  • The application was unable to accurately process the company’s billion-plus transaction records on the Oracle Fusion Cloud to generate a report on the inventory position.
  • It was also challenging to use an external tool to do this as it would take several days to extract data from the Oracle cloud to an external source while facing multiple failures during the process.
  • This would make the cost and quality reconciliation of copies of books lying in different warehouses and distribution centres across the world very difficult and time-consuming, as business users did not have on-time and accurate visibility of the on-hand quantity.
  • In turn, this had adverse business consequences such as inaccurate planning, higher inventory costs, and inefficient fulfilment.

The company reached out to Relevance Lab for a solution. Our SPECTRA platform automated and optimized the process of data ingestion, harmonization, transformation, and processing, keeping in mind the specific circumstances of the client. The deployment yielded multiple benefits:


  • On-Hand quantity and costing reports are now generated in less than an hour
  • Users can access raw data as well as multiple reports with near real-time data, giving them full flexibility and making the business more responsive to market dynamics
  • Overall, user efforts stand reduced by 150 hours per person per quarter by using SPECTRA for their inventory report, leading to higher productivity
  • With all the raw data in SPECTRA, several reconciliation procedures are in place to identify missing data between the Oracle cloud and its legacy system

The Hadoop-based architecture can be scaled flexibly in response to the continuously growing size of the transaction database and is also compatible with the client’s future technology roadmap.


Conclusion
RL’s big-data platform, SPECTRA, offers an effective and efficient future-ready solution to the reporting challenges in Oracle EBS when dealing with large data sets. SPECTRA enables clients to access near real-time insights from their big data stored on the Oracle Cloud while delivering substantial cost and time savings.

To know more about our solutions or to book a call with us, please write to marketing@relevancelab.com.



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2020 Blog, Analytics, Blog, Featured, Spectra Blog

The Consumer Packaged Goods (CPG) Industry is one of the largest industries on the planet. From food and beverage to clothes to stationary, it is impossible to think of a moment in our lives without being touched or influenced by this sector. If there is one paradigm around which the industry revolves, regardless of the sub-sector or the geography, it is the fear of stock outs. Studies indicate that when a customer finds a product unavailable, 31% are likely to switch over to a competitor when it happens for the first time. It becomes 50% when this occurs for a second time and rises to 70% when this happens for a third time.

Historically, the panacea for this problem has been to overstock. While this reduced the risk of stock outs to a great extent, it induced a high cost for holding the inventory and increased risk of obsolescence. It also created a shortage of working capital since a part of it is always locked away in holding excess inventory. This additional cost is often passed on to the end customer. Over time, an integrated planning solution which could predict demand, supply and inventory positions became a key differentiator in the CPG industry since it helped rein in costs and become competitive in an industry which is extremely price sensitive.

Although theoretically, a planning solution should have been able to solve the inventory puzzle, practically, a lot of challenges kept limiting its efficacy. Conventional planning solutions have been built based on local planning practices. Such planning solutions have had challenges negotiating the complex demand patterns of the customers which are influenced by general consumer behaviour and also seasonal trends in the global market. As a result the excess inventory problem stays, which gets exacerbated at times due to bullwhip effect.

This is where the importance of a global integrated Production Sales Inventory (PSI) solutions comes in. But usually, this is easier said than done. Large organizations face multiple practical challenges when they attempt to implement this. Following are the typical challenges that large organizations face


  • Infrastructural Limitations
    Using conventional systems of Business Intelligence of Planning systems would require very heavy investment in infrastructure and systems. Also the results may not be proportionate to the investments made.
  • Data Silos
    PSI requires data from different departments including sales, production, and procurement/sourcing. Even if the organization has a common ERP, the processes and practices in each department might make it difficult to combine data and get insights.
    Another significant hurdle is the fact that larger organizations usually tend to have multiple ERPs for handling local transactions aligned to geographical markets. Each ERP or data source which does not talk to other systems becomes siloed. The complexities increase when the data formats and tables are incompatible, especially, when the ERPs are from different vendors.
  • Manual Effort
    Harmonizing the data from multiple systems and making them coherent involves a huge manual effort in designing, building, testing and deployment if we follow conventional mode. The prohibitive costs involved, not to mention the human effort involved is a huge challenge for most organizations.

Relevance Lab has helped multiple customers tide over the above challenges and get a faster return on their investments.

Here are the steps we follow to achieve a responsive global supply chain

  • Gather Data: Collate data from all relevant systems
    Leveraging data from as many relevant sources (both internal and external) as possible is one of the most important steps in ensuring a responsive global supply chain. The challenge of handling the huge data volume is addressed through the use of big data technologies. The data gathered is then cleansed and harmonized using SPECTRA, Relevancelab big data/analytics platform. SPECTRA can then combine the relevant data from multiple sources, and refresh the results at specified periodic intervals. One point of note here is that Master Data harmonization, that usually consumes months of effort can be significantly accelerated with the SPECTRA’s machine learning and NLP capabilities.

  • Gain Insights: Know the as-is states from intuitive visualizations
    The data pulled in from various sources can be combined to see the snapshot of inventory levels across the supply chain. SPECTRA’s built-in data models and quasi plug and play visualizations ensure that users get a quick and accurate picture of their supply chain. Starting with a bird’s eye view of the current inventory levels across various types of stocking locations and across each inventory type, the visualization capabilities of SPECTRA can be leveraged to have a granular view of the current inventory positions or backlog orders or compare sales with the forecasts. This a critical step in the overall process as this helps organizations to clearly define their problems and identify likely end states. For example, the organization could go for a deeper analysis to identify slow moving and obsolete inventory or fine tune their planning parameters.

  • Predict: Use big data to predict inventory levels
    The data from various systems can be used to predict the likely inventory levels based on service level targets, demand predictions, production and procurement information. Time series analysis is used to predict the lead time for production and procurement. Projected inventory level calculations for future days/weeks, thus calculated, is more likely to reflect the actual inventory levels since the uncertainties, both external and internal, have been well accounted for.

  • Act: Measurement and Continuous Improvement
    Inventory management is a continuous process. The above steps would provide a framework for measuring and tracking the performance of the inventory management solution and make necessary course corrections based on real time feedback.

Conclusion
Successful inventory management is one of the basic requirements for financial success for companies in the Consumer Packaged Goods Sector. There is no perfect solution to achieve this as the customer needs and environment are dynamic and the optimal solution could only be reached iteratively. Relevancelab framework to address inventory management combining deep domain experience with SPECTRA’s capabilities like NLP for faster master data management & harmonization, pre-built data models, quasi plug and play visualizations and custom algorithms offer a faster turn-around and quicker Return-on-Investment. Additionally, the comprehensive process ensures that the data is massaged and prepped for both broader and deeper analysis of the supply chain and risk in the future.

Additional references
https://www.2flow.ie/news-and-blog/solving-the-out-of-stock-problem-infographic

To learn how you can leverage ML and AI within your customer retention strategy, please reach out to marketing@relevancelab.com



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2020 Blog, Analytics, Blog, Featured, Spectra Blog

If you are a business with a digital product or a subscription model, then you are already familiar with this key metric – “Customer Churn”.

Customer Churn is the percentage of customers who stopped using your product during a given period. This is a critical metric, as it not only reflects customer satisfaction but it also has a big impact on your bottom line. A common rule of the thumb is that it costs 6-7 times to get a new customer versus keeping the customers you already have. In addition, existing customers are expected to spend more over time, and satisfied customers lead to additional sales through referrals. Market studies show that increasing customer retention by small percentage can boost revenues significantly. Further research reveals that most professionals consider that Churn is just as or more important a metric than new customer acquisitions.

Subscription businesses strongly believe customers cancel for reasons that could be managed or fixed. “Customer Retention” is the set of strategies and actions that a company follows to keep existing customers from churning. Employing a data-driven customer retention strategy, and leveraging the power of big data and machine learning, offer significant opportunities for businesses to create a competitive advantage versus their peers that don’t.

Relevance Lab (RL) recently helped a large US based Digital learning company benefit from a detailed churn analysis of its subscription customers, by leveraging the RL SPECTRA platform with machine learning. The portfolio included several digital subscription products used in school educational curriculums which are renewed annually during the start of the school calendar year. Each year, there were several customers that did not renew their licenses and importantly, this happened at the end of the subscription cycle; typically too late for the sales team to respond effectively.

Here are the steps that the organisation took along the churn management journey.



  • Gather multiple data points to generate better insights
    As with any analysis, to figure out where your churn is coming from, you need to keep track of the right data. Especially with machine learning initiatives, the algorithms depend on large quantities of raw data to learn complex patterns. A sample list of data attributes could include online interactions with the product, clicks, page views, test scores, incident reports, payment information, etc, it could also include unstructured data elements such as reports, reviews and blog posts.

    In this particular example, the data was pulled from four different databases which contained the product platform data for our relevant geography. Data collected included product features, sales and renewal numbers, as well as student product usage, test performance statistics etc, going back to the past 4 years.

    Next, the data was cleansed to remove trial licenses, dummy tests etc, and to normalize missing data. Finally, the data was harmonized to bring all the information into a consolidated format.

    All the above pipelines were established using the SPECTRA ETL process. Now there was a fully functional data setup with cleaned data ordered in tables, to be used in the machine learning algorithms for churn prediction.

  • Predictive analytics use Machine Learning to know who is at risk
    Once you have the data, you are now ready to work on the core of your analysis, to understand where the risk of churn is coming from, and hence identify the opportunities for strengthening your customer relationships. Machine learning techniques are especially suited to this task, as they can churn massive amounts of historical data to learn about customer behavior, and then use this training to make predictions about important outcomes such as retention.

    On our assignment, the RL team tried out a number of machine learning models built-in within SPECTRA to predict the churn and zeroed in on a random forest model. This method is very effective when using inconsistent data sets, where the system can handle differences in behavior very effectively by creating a large number of random trees. In the end, the system provided a predicted rating for each customer to drop out of the system and highlighted the ones most at risk.

  • Define the most valuable customers
    Parallel to identifying customers at risk of churn, data can also be used to segment customers into different groups to identify how each group interacts with your product. In addition, data regarding frequency of purchase, purchase value, product coverage helps you to quickly identify which type of customers are driving the most revenue, versus customers which are a poor fit for your product. This will then allow you to adopt different communication and servicing strategies for each group, and to retain your most valuable customers.

    By combining our machine learning model output with the segmentation exercise, the result was a dynamic dashboard, which could be sorted/filtered by different criteria such as customer size and geographical location. This provided the opportunity to highlight the customers which were at the highest risk, from the joint viewpoint of attrition and revenue loss. This in turn enabled the client to effectively utilize sales team resources in the best possible manner.

  • Engage with the customers
    Now that you have identified your top customers who you are at risk of losing, the next step is to actively engage with them, to incentivise the customers to stay with you, by being able to help the customer achieve real value out of your product.

    The nature of engagement could depend on the stage the customer is in the relationship. Is the customer in the early stage of product adoption? This could then point to the fact that the customer is unable to get set up with your product. Here, you have to make sure that the customer has access to enough training material, maybe the customer requires additional onboarding support.

    If the customer is in the middle stage, it could be that the customer is not realizing enough business value out of your product. Here, you need to check in with your customer, to see whether they are making enough progress towards their goals. If the customer is in late stage, it is possible that they are looking at competitor offerings, or they were frustrated with bugs, and hence the discussion would need to be shaped accordingly.

    To tailor the nature of your conversation, you need to take a close look at the customer product interaction metrics. In our example, all the customer usage patterns, test performance, books read, word literacy, etc, were collected and presented as a dashboard, as a single point of reference for the sales and marketing team to easily review customer engagement levels, to be able to connect constructively with the customer management.


Conclusion
If you are looking at reducing your customer churn and improving customer retention, it all comes down to predicting customers at risk of churn, analyzing the reasons behind churn, and then taking appropriate action. Machine learning based models are of particular help here, as they can take into account hundreds and even thousands of different factors, which may not be obvious or even possible to track for a human analyst. In this example, the SPECTRA platform helped the client sales team to predict the customers’ inclination for renewal of the specific learning product with 92% accuracy.

Additional references
Research from Bain and Co. shows that increasing customer retention by even 5% boosts revenues by 25% – 95%
Reportfrom Brightback reveals Churn is just as or more important a metric than new customer acquisitions

To learn how you can leverage machine learning and AI within your customer retention strategy, please reach out to marketing@relevancelab.com



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