LatentView Wins again!
LatentView’s affair with awards and recognition continues. After an eventful 2009, where we were recognized as one of the fastest growing companies in Asia by Deloitte and Red Herring, LatentView has begun 2010 with a bang. This time, British Airways that has recognized LatentView by conferring its Business Opportunity Grants.
The Opportunity Grants Program is an initiative by the British Airways as a part of its commitment to encourage entrepreneurship from the SME community. Applicants were evaluated on criteria like type of business, the importance they lay on face-to-face meetings and how facilitating frequent business travel would add impetus to their business. Winners are awarded 10 return flights over the coming year.
LatentView is delighted to be among the winning list of 50 companies from India. The philosophy behind the grant is a case study by itself, in our core area of analytical insights! The BA Opportunity Grants program was created after a recent Harvard Business Review Analytical Services study revealed that business people believed that face-to-face meetings were the key to success in building and maintaining long term client relationships.
LatentView is a Winner in RedHerring 100 Asia
LatentView Analytics Pvt. Ltd. is delighted to announce that we are a recipient of the Red Herring 100 Asia, an award given to the top 100 private technology companies based in the region.
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LatentView Ranked 3rd in Deloitte Technology Fast 50 India 2009
LatentView is delighted to announce that we have been recognized as one of the 50 most rapidly growing companies in India.
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Press coverage here:
Business Standard
The Economic Times
Rediff.com
Keys to Analytics Success
In a flat world, where traditional sources of competitive advantage are rapidly eroding, analytics holds out the promise of being one of the last points of differentiation. However, as with any strategy, competing on Analytics requires certain ingredients and processes as pre-requisites. Otherwise, there is a good chance that you will spend tons of $$$ and effort with nothing to show for.
From our experience of helping our clients, here are some recommendations to profit from predictive analytocs.
1. Think Big, Start Small. People tend to think of predictive analytics as just another initiative on the tactical side of the business. Viewing it this way leads to underestimating its impact and the data and process change management effort required to profit from it. To maximize the value from analytics initiatives, organizations need to focus on their distinctive capabilities, ensure that solutions are business driven, and start small
Focus on your distinctive capabilities. Given the breadth of analytical possibilities, you should prioritize your analytics initiatives on enhancing your chosen distinctive capabilities - resources that provide the basis for your competitive advantage. If your distinctive capability lies in your ability to attract and keep the most profitable customers, you should focus on analytics initiatives that help you attract and retain the right customers and maximize the value of your customer relationships
Business-driven initiatives. Profiting from insights generated by predictive analytics may require your organization to adapt your business processes to take advantages of these insights. Hence, these initiatives succeed only when they are aligned to organization's goals and have the top management's buy-in. Business needs, rather than other considerations, should drive analytics initiatives, not the other way
Do not boil the ocean. At the risk of stressing the obvious, we advise you to start small, go for low hanging fruits, and balance faster time to market with solution complexity (data availability, process changes needed, etc.)
2. Define the Business Case. Identify a list of business pain points that can be addressed through analytics
a) Define the objectives. Define the problem in clear terms, using key performance indicators. Be SMART about your objectives – specific, measurable, achievable and realistic, and time-bound. For example, if your issue is the high attrition rate for a key segment of your subscribers, define the current and targeted retention rate, quantify the gap, and the time by which the gap is proposed to be closed
b) Define your solution approach. Clearly describe how the proposed analytics solution is expected to address the gap. Outline the data requirements and the process changes that might be needed to ensure that insights from analytics are used for making decisions in the process
c) Define the costs and benefits. Create multiple scenarios of success for the analytics initiatives – conservative, moderate and aggressive. For each of these scenarios, identify the expected lift from the solution, the data needed, the process changes needed, the complexity of the analytics models, and attach a cost to each of these elements, and compare it against the expected dollar benefits. If possible, perform a Net Present Value (NPV) analysis to identify if the project is worth devoting time and effort
d) Prioritize. Based on the cost/benefit considerations and the time to market, prioritize your initiatives. Assign resources and responsibilities to coordinate solution definition, development and implementation
3. Ensure you have the right resources. Ensure that you have access to the right resources - data, tools, and people
Data. Data is the foundation of predictive analytics. Ideally, more the data, the better. However, what is more important is the quality of the data. Key dimensions that you typically need include (in decreasing order of importance) customer transaction history, customer demographics, customer interaction and attitudinal data
Tools. To maximize productivity, invest on tools that are widely used (so it is easier to get people and build a team), that have a large variety of algorithms, and which can scale to manage your data. When it comes to tools there are a variety of options – a later post will address some of these options
People. When it comes to people, make sure that you invest heavily in smart analysts who have the business acumen, and who can intelligently tie together all the terabytes of customer behavior, demographics and interaction data into bite-sized chunks of insights. Otherwise, you run the risk of drowning in data while starving for insights. As a rule of thumb, I recommend that you follow the 10/90 rule suggested by Avinash Kaushik (applied to Web Analytics, it’s probably applicable to other domains as well)
4. View Analytics as a Discipline. Predictive analytics is no silver bullet, and profiting from it requires a disciplined approach. We recommend a disciplined approach with a clear plan of action, and follow-through to ensure execution excellence
Define a plan and a methodology. Create a detailed plan, with milestones, resources and responsibilities. The plan should ideally have four phases: data preparation, data exploration, model development and validation, and solution implementation. Define a methodology for project execution based on industry standards, such as CRISP-DM. and break down the project into doable tasks that can be assigned to people
Follow-through. Ensure that you execute well. Look for deviations from desired tolerances, and rigorously work to close them. Conduct team meetings to question and follow-through on the tasks, to ensure that schedule and deliverables are under control. Validate the insights that are generated from these models with the business managers based on their experience and knowledge
5. Expect incremental progress. According to experts in the industry, organizations that go for the gold rush with predictive analytics are bound to become frustrated from the weight of massive, unrealistic expectations. The reality is that predictive analytics provides benefits in little percentages, rather than unexpected, multi-million dollar discoveries.
Be prepared to persist with delivering incremental improvements to business processes or customer insights that generate accumulating value over the medium term.
These recommendations capture some of our thinking on how best to use predictive analytics. Do you agree or disagree? Would love to hear from you.
A Visual Programming Environment for R
A much needed visual programming environment for R has been released. I have not downloaded and used Red-R yet, but we can see its potential for productivity improvements.
Apart from the Open Source Weka, several commercial (and expensive) data mining tools already offer a visual environment for predictive analytics. However, as a great fan of R, I'm happy that R offers this now. Expect to hear from us soon on this more
IBM to acquire SPSS
IBM announced its intention to acquire SPSS - a leading provider of Predictive Analytics & Optimization software. This is a significant event for clients wishing to deploy predictive analytics solutions.
With SPSS, IBM has a complete suite of products across the data analytics spectrum - ETL (Ascential), database (DB2), predictive analytics & optimization (SPSS), in-database mining (IBM Intelligent Miner) and a visual workbench for predictive analytics (SPSS Clementine). This, combined with IBM global services, can offer a compelling combination of integrated hardware, software and services.
With this acquisition, IBM is bound to integrate SPSS with its popular database software, which could impart capabilities to work with large databases. Today, SAS is the only vendor that integrates data management, reporting, predictive analytics, optimization, and an intuitive user interface into a single integrated package. There are other vendors too - including KXEN, Oracle, Statistica, etc. who have different vision and execution strengths. The most serious challenger amongst them is Oracle. Oracle is continuously improving its data mining capabilities, providing a robust data mining work-bench, a reasonable set of algorithms, the ability to handle large data volumes, and a fully integrated suite of products. Even pure-play Business Intelligence (BI) vendors such as MicroStrategy, offer the ability to score predictive models developed using other packages (through PMML). Open Source tools such as R, Weka and RapidMiner offer the ability to build advanced data mining solutions through cutting-edge algorithms. However, they do not have the ability to work with large databases (this is changing).
Given this scenario, IBM's acquisition is bound to challenge the market leader, SAS, in the predictive analytics and optimization space. This is good news for all those who value diversity (clients and solution providers).
Retail Analytics: Plenty of Opportunities
Traditionally, Retail industry has been characterized by intense competitive pressures and lower margins. This is probably due to a variety of factors - such as advent of new formats, blurring of category boundaries, growth of specialist retailers, and reducing consumer loyalty with better access to information. In this post, we will talk about what retailers need to do to attract and keep their best customers, reduce costs and improve profitability.
Typically, modern retail involves huge investments in IT infrastructure - point-of-sale, inventory, and supply chain management systems. Such transactional systems automate business processes, thereby improving the efficiency, productivity and agility of the business. In addition to these systems, retailers also invest in Business Intelligence capabilities - tools and process to extract, cleanse, transform and summarize the data into data-warehouses, and querying and reporting capabilities.
While these investments are critical to enabling their business processes, BI helps retailers "look back", rather than "look forward". For instance, using BI, a merchandizing manager can evaluate the impact of promotions or pricing, comparing it against what happened in the previous year or month, or against a forecast. As we will see soon, this is simply a superficial way of looking at promotions.
However, predictive analytics enables a retailer look forward, rather than simply look back, or look deeper, rather than just apparent
phenomena. To give an example of the latter, predictive analytics is used by leading retailers to unravel complex relationships in the promotions, so that they can understand the true impact of promotions after accounting for cannibalization, forward buying, switching behavior, competitor activity, brand pull, etc.), and make optimal decisions based on these insights, rather than simply relying on apparent lift.
Visionary retailers use predictive analytics to differentiate themselves in their chosen areas (distinctive capabilities) - whether it is in maintaining cost advantage, or in providing a rich shopper experience (depth & breadth of assortment, in-store service, etc.), or in attracting and retaining loyal customers (through loyalty programs and targeted offers). Some of the key processes where predictive analytics and optimization can help a retailer include:
- Consumer Insights - Understand your consumers and shoppers, their motivations, and behavior
- Assortment Optimization and Shelf Space Allocation - You probably know that 20% of your SKU's is driving 80% of your volumes (or a variant of this theme). However, how can you keep those SKU's that contribute to your value or price image for a given segment
- Promotions Optimization - Maximize the effectiveness of your promotions
- Demand Planning - Predict your demand and keep only what would sell
- Store localization - Customize your store assortment to local tastes and preferences
- Pricing Strategies - Extract the maximum price that customers are willing to pay
- Create an analytics vision, road-map and goals
- Develop a step-by-step methodology to profit from predictive analytics
- Invest in clean data tools & technologies
- Invest in people and services
- Focus on store-level insights and actions
- Get help from outside
- Create a data-driven, test & learn culture
We would have plenty of opportunities to talk on each of these points in future posts
Looking at our own-backyard
“I know half of my advertisement works, and I also know which half”. Don’t go back, you read it right! Technological advancements in online direct marketing have made it possible to evaluate in real time the success/failiure of any online media campaign. In fact marketers are now using technology not just for evaluating campaign effectiveness but also to design them.
Through "Hyper-targeting" - a recent development in online direct marketing – marketers are collecting vital information of online users like relationship status, hobbies, health information ,demographics etc to design customized ad-messages that’s relevant for the audience. Social networking sites, online interactive entertainment etc has surely given a boost to hyper-targeting by gently prodding the users to share more information online and ensuring that the information is shared with the advertisers to enable them to create effective advertisements. As always the case with any data-backed decisions, "Hyper-targeting" is only as good as the data which lie beneath it.
With data integrity being a key factor, an ideal testing ground for "Hyper-targeting" campaigns would be the advertiser’s own backyard. Advertisers can put their traditional data-storage capabilities to good use by mining relevant information to create targeted "Hyper-targeting" campaigns, to be run on the advertiser’s own website/homepage, for their existing customers. This would be particularly relevant for BFSI players, like banks, who maintain an exhaustive data base of their customers and also have an online banking facility with sizeable user visits.
In fact such "Hyper-targeting" campaigns can open a new interactive and intelligent marketing channel for advertiser’s, using the space and information which rests in their own backyard.
CPG & Retail in India – A Rapidly Changing Landscape
Over the last few decades, fast moving consumer goods in India were predominantly distributed through neighborhood stores (kirana stores). These stores were small (about 100 sq ft.), and were owned and run by family members. Customers were mostly from the immediate neighbourhood and were usually well known to the store owners, the assortment was limited, and customers were typically loyal. The stores provided convenience in the form of easy credit and free home delivery for bulk purchases. Organized retail had no presence, departmental stores, super markets, hyper-markets and malls were never heard of, and the shopping experience was seen as something that was necessary.
However, in the last 15 years, Indian retail scenario has undergone a dramatic transformationand has witnessed rapid growth across the country. Initially, growth was focused on the southern cities, before it spread to the rest of the country. As retail industry provides one of the largest sources of employment in India, majority stake by foreign investors (FDI's) are not permitted, except in single brand outlets. Hence, organized retail is dominated by local players - such as Future Group, RPG Enterprises, Reliance Industries, Aditya Birla Group, Tata Sons, and Bharti Group.
The size of the retail industry in India is estimated to be of the order of US$ 300 - US$400 billion per year. Organised retailing comprises only about 3% of the total, and is estimated at around US$ 8.7 billion. This represents both an opportunity and a challenge for organized retailers. The need for heavy investments in real estate, technology, warehouses, and other infrastructure, and lack of qualified manpower are all challenges that need to be addressed. However, changing shopper behavior, favourable demographics, increasing purchasing power and the government's emphasis on upgrading education and physical infrastructure, can mitigate these challenges and present opportunities for growth.
To make inroads, organized retailers need to attract consumers with winning strategies - provide better value for money, shopping experience, greater choice of assortment, and better quality, while managing the costs required to do business. In this scenario, retailers have to make the right investments in technology and processes to gather accurate data around consumers, suppliers, purchases, employees, and stores. By themselves, these are necessary and important, however, none of this is going to create a lasting competitive advantage.
Above all, what would separate a winning retailer from the rest of the pack is a culture of data-driven decision making. This would enable retailers to acquire and retain the right customers, make the right pricing, promotion and product placement decisions, develop effective collaborative relationships with their suppliers and hire the right employees and providing the right incentives that motivate them to perform.
LatentView aspires to partner with retailers and CPG manufacturers in India to help them develop a consumer-centric approach to retailing.
IBM makes a foray into Predictive Analytics
IBM announced that it is starting to offer business analytics and optimization services as part of its consulting arm - IBM Global Services. This is big news for all of us in the Predictive Analytics consulting space - entry of IBM provides market validation that predictive analytics is becoming a critical imperative for businesses to maintain their competitive advantage.