• FB Workplace buzzes at TEG

    Why FB Workplace – An Ideal Enterprise Networking Platform for Startups?

    Monica woke up on a Monday morning in her cosy double bed. Already delayed than her usual schedule, she hurriedly got ready, ate her breakfast and rushed for her workplace. On her way, she realized that she has to update the company employees with the newest trend in Artificial Intelligence and the categorical shift the analytics industry will be taking in automating the process in the future. Drafting a mail and sending it across to all the employees at various locations, including those in client locations was a cumbersome task, would require intervention of the centralized mail desk… blah… blah… blah… But what occurred to her was that her company, TEG Analytics, was a Workplace user. She opened the workplace app from her phone and updated a new post with the link to her article and BINGO!! Within 15 minutes there were more than 25 views.

    4PM and she was sorted for the day. TEG’s annual Cricket Tournament was supposed to start in the next 10 minutes.

    “Arvind wanted to watch the tournament”, she jumped off her seat as she got ready to post a live video in TEG workplace the moment the match started.

    “Damn! Girish dropped yet another catch?” gasped a retired hurt Satya, Captain of Team Andhra Chargers, from his workstation, as he watched the video at his workstation at the other end of the office.

    Welcome to the new age of Enterprise Networking, better known as “Workplace”. After 20 months in a closed beta under the working title Facebook at Work, Facebook has finally brought its enterprise-focused messaging and networking service to market under a new name, Workplace – a platform which connects everyone at your company to turn ideas into action.

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    Workplace – which is launching as a desktop and mobile app with News Feed, Groups both for your own company and with others, Chat direct messaging, Live video, Reactions, translation features, and video and audio calling — is now opening up to anyone to use, and the operative
    word here is “anyone”. This means that Workplace won’t only cater to the desk dwelling “researchers” of the company who are brainstorming every day for insights of the industry, in their air-conditioned cabins, but also the more “naive” machine handlers, people whose work involve travel, and everyone who have been rarely included into an organization’s greater digital collaborations. TEG has been very active in using all the features extensively, be it knowing about an employee profile who has recently joined, be it planning their Marketing strategy in a closed Marketing Group, or be it creating events with calendar (date/time) details.

    What’s more is that, Workplace wants to build itself “the Facebook way” with a unique twist. As explained by Julien Codorniou, director of Workplace, in an interview in London, “we had to build this totally separate from Facebook, and we had to test and get all the possible certifications to be a SaaS vendor”. Workplace has been tested in every milieu ranging from the most dynamic MNCs to the rather conservative government agencies. In such a scenario, it provides the perfect enterprise social networking platform for the Indian start-up market. What’s better is that Workplace is an ad-free space, separate from your personal Facebook account – hence nothing to distract you. Also, with Workplace designed in the same model as Facebook, people with not much exposure to enterprise networking find this interactive and easy to handle.

    Facebook has signed up around 800 clients in India including Bharti Airtel and Jet Airways for its workplace version, making the country one of the top 5 in the world for the enterprise communication app. It counts Godrej, Pidilite, MakeMyTrip, StoreKing and Jugnoo as some of its top clients in India.

    “We see it is as a different way of running the company by giving everyone a voice, even people who have never had email or a desktop before,” said Julien Codorniou, VP-Workplace by Facebook, which competes with Google, Microsoft and Slack in the office-communication segment. “Every company where you see desk-less workers, mobile-only workers is perfect for us. That is why I think there is a strong appetite for Workplace in India compared to other regions. It is a huge market,” said Codorniou. “Mobile first is a global strategy, but it resonates well with Indian companies.” Facebook says that the Indian workforce below the age of 25 years

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    prefers using mobile applications to communicate rather than emails. Here’s where Workplace wins.

    Another innovative prospect of Workplace is its pricing model, compared to its competitors like Yammer, Slack, etc. Workplace, unlike its competitors which has different rates for low end basic features and high end features, provides all the features to its users at the same rate. It charges monthly depending on the number of active users a company has in that month, active meaning, having opened and used at least once the Workplace account in the month.

    Most enterprise social platforms fail to achieve broad traction because they don’t offer ready answers to “how” & “how much” questions. With Facebook’s announcement about the integrations with Box, Microsoft or Dropbox or even Quip/Salesforce turning true; Workplace will be the all-you-need Enterprise Networking platform. Eventually, at the end of the day, if you don’t integrate with the tools your customer use, you’re going to lose a customer – and that’s not a very positive payoff.

    Certainly, with a brand like Facebook, which has over the years captured people’s imagination and flattered people with their innovative approaches, endorsing Workplace, this seems an interesting concept. It still needs to be seen how they fare in a completely different platform, the Enterprise Social Network, and the way TEG is using it will help figure out the drawbacks and potentials.

  • Madalasa Venkataraman (Madhu)

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    1) How did you get interested in working with data?

    I think it’s a personality defect. I am sure my parents despaired of me listening to anything without a sound logically constructed argument.
    I was never one to work on gut feel and was more of a ‘rationalist’ in my college days – I would never accept anything anyone said without proof, or at least without a debate backed by numbers.

    Somewhere along the way, I got into analyzing data just for the heck of it. Cost/benefit analysis, the heuristic optimizations that we do on an everyday basis – these fascinated me. And then I discovered microeconomics and finance – there was a whole world out there that discussed rational decision making in terms of utility functions!  Suddenly, when I learnt statistics, things sort of fell into place, the inherent conflicts in my data analysis and methodologies started having a name and a theory behind them. That was a moment of revelation (as much as passing the first stats course was :)
    To me, data represents a move towards a single truth – a unified view that just ‘is’, the layers and stories it reveals and hides is simply fascinating. Everything that happens, that bugs us, that needs solving, the tools are just there to help us solve, if we have the data. Data science is the medley of statistics meets business meets urgent problems that need to be solved, and that calls out to me.

    I didn’t set out to be a data scientist, and I didn’t set out to be a geek (honestly!). But when training meets passion, the possibilities are endless. Add belief to the mix – the relevance of data sciences and its ability to influence policy, business and I think that’s a winning combination.

    2) What are your principal responsibilities as a data scientist?

    I lead the Stats team at TEG Analytics. My role of to build the team, to make sure we build TEG’s competence in information storage and retrieval, statistical analysis, visualization and in business insights. – I get involved in projects, we brainstorm and innovate, and come up with amazing solutions that are state of art, cutting edge – and with relevance to the business context, the business issue/case we are trying to solve.

    3) What innovations have you brought into this role?

    The way I perceive my role is probably a little different to the traditional data scientist role. I am also here to invite our talent into a world of wonderful global innovations in machine learning, in AI, in building the next generation or suite of products and solutions that will solve real world business problems, to inspire them to reach beyond their current projects, to read and to upskill with ravenous hunger. I come from a teaching background. I have been a professor in business studies, and I work together with our teams to build a consulting perspective to our solutions across domains.

    4) Can you share examples of any interesting projects where data science played a crucial role?

    Some recent ones that have been interesting and challenging
    1. A brand juice sentiment analysis project. This was interesting because of the complexities in the data and in the interpretation of sentiment scores.
    2. A Medicare plan competitiveness analysis based on publicly available CMS data, using which we predicted enrollments in Medicare plans mimicking customers choice models.

    5) Any words of wisdom for Data Science students or practitioners starting out?

    More often than not, data science is seen entirely as a statistical/analytics effort, or as a business problem where numbers are incidental to the story. Data sciences is cross-disciplinary in nature – we need the stats acumen, and the business insights. Domain knowledge is essential – be willing to invest in it, as long as it takes. Knowing the right program and package is cool; to stitch the story together and influence budgetary allocations is more so.

    6) What Data Science methods have you found most helpful?

    Common sense, but that’s not really a data science method. I can’t call out a specific method – I personally like to use a judicious mix of parametric and model-free techniques, depending on the case. On a more serious note, irrespective of the method, or the machine learning, or the neural network package, there is merit in covering the basics. A data dictionary, good foundations, EDA and good design of experiments are mandatory. The rest is really going to change based on the task at hand.

    7) What are your favorite tools / applications to work with?

    I have used a variety of tools. I like Stata quite a lot. I am often asked if R is a better bet than SAS. SAS is a very powerful, accurate tool – its advantage is, if the program runs, the results are pretty much what you are looking for. In R, due to the multitude of packages, it’s easy for beginners to get confused, and the results are more dependent on the programmer’s skill levels.

    8) With data science permeating nearly every industry, what are you most excited to see in the future?

    IOT and AI are converging in a big way. There is tremendous potential, it’s an exciting field. Geo-spatial data is already big, it will get bigger with drone technology and geo-spatial visualization is a great field to look forward to.
    In the sales and marketing analytics field AI/NN models for relevant 1:1 personalization, multi-touch attribution in media efficiencies, hidden Markov models/LSTM for sequence learning in text analysis – these are some of the things to look out for.

    9) What lessons have you learned during your career that you would share with aspiring data scientists entering the field?

    Three things I believe are important: First -  Business trumps statistics, and that’s the natural order of this world. Second -The solution should be as complex as necessary, and no more – it’s important to embrace Occam’s razor. Fast failure is more important than the perfect model.
    Third – and most important. There are principles and theories in statistics, information modelling, databases – and there are tools and techniques. It is imperative to keep oneself updated on the tools and programs and applications, but always to relate it back to the fundamentals, the principles and the theory.

  • Retail Demand Forecasting

    How to develop an Effective Scientific Retail Demand Forecast?
    Purpose of the Forecast
    The ability to effectively forecast demand is critical to the success of a retailer. demand forecasting is especially important in the retail industry because it leads to …… lower inventory costs, faster cash turnover cycles, quicker response to trends, etc etc. Retailers require forecasts that would be instrumental in directing the organisation through a minefield of capacity constraints, multiple sales geographies and a multi-tier distribution channel. A robust demand forecast engine will significantly impact both top & bottom lines positively.

    Demand forecasting helps understand key questions viz. which market would place demands for which specific type of product, which manufacturing unit should cater to which retailer, how many product units are required in a given season etc.? Given the sophisticated tools & techniques available today, all retailers should replace gut based decision making with scientific forecasts. The benefits, throughout the lifecycle of the analysis will far outweigh the one time set up and ongoing maintenance costs. There is a lot of value in answering these questions through scientific methodologies as compared to educated guesses, or judgmental forecasts.

    Business Benefits
    Scientific forecasting generates demand forecasts which are more realistic, accurate and tailored to specific retail business area. It facilitates optimal decision-making at the headquarters, regional and local levels, leading to much lesser costs, higher revenues, better customer service and loyalty.

    Range of Business Users
    Traditionally, only the sales department has used forecasts, but in evolved markets the usage of forecasts is now pan organizational. Sales Revenue Forecasting, Marketing & Promotion Planning, Operations Planning, Inventory Management etc. also extensively use sales forecasts. Indian retail needs to imbibe this discipline as their scale of operations grows larger and they are unable to cope with the entrepreneurial style of functioning, which was the key to their success in the start up phase.

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    Typical Challenges Faced!
    Though demand forecasting is an important aspect of a retail business, more often than not, it is laced with multiple challenges. Some of them could be:

    Level/Scope of the Forecasts
    A large retailer may have thousands of SKUs. A conscious decision has to be made regarding the product hierarchy level at which the forecasts are needed, as it is very challenging to produce forecasts for all existing SKUs, neither does it make sound financial sense in most cases. Other concern would be the number of stores a typical large retailer possesses, and whether a separate forecast is needed for each of the stores.

    In order to optimise the cost-benefit, TEG recommends creation of forecasts at the “Store-Cluster” & “SKU-Cluster” levels. The store clusters are created using store characteristics, like past demand patterns and local/ regional demand factors. The SKU clusters are determined by the category type, life cycle etc.

    New Product Forecasts
    A retailer typically launches new products every month/season. Using past data to forecast is not feasible, as past data does not exist. TEG, would tackle the situation by considering complementary products, based on their key characteristics like target segment, product category, price level, features etc. A rapidly emerging methodology is the estimation of future demand using Advanced Bayesian Models (Fig. 3).

    Bizarre/Missing Historic Sales Pattern
    The erratic sales figures for many items in the store often pose a lot of issues for scientific methods of forecasting. In these situations, we need to resort to extensive statistical data cleaning exercises.

    Non-availability of True Historic Demand
    Historic sales are used to estimate the future demand, as it is the only reliable quantitative indicator available about customer demand. However it is possible that sales data end-up with a bias because of the inventory rupture or temporary promotional activities. These situations need correction to sales history to reflect the true demand. Since demand bias is very business specific, such corrections usually require in-depth domain expertise to interpolate/extrapolate the sales figures.

    Forecasting Techniques
    Demand forecasting techniques are broadly divided into two categories: Judgmental and Statistical.

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    The Scientific (Statistical) Forecast Models
    Scientific models are divided into two categories, Extrapolation Models & Causal Models (Fig. 2). The extrapolation models are based exclusively on the past/historic sales data where the trend, seasonality & cyclicity prevalent in the historic sales data are examined to project the sales in future. However it is pretty intuitive that the future sales not only depend on the past sales but also on the other factors viz. economic trends, competitors’ movement, festive events, promotional activities etc. In order to incorporate such external factors in forecasting, a variety of causal models are available. In absence of such external factors’ data, the extrapolation models provide decent forecasts in most of the situations.

    Key Comparisons of Various Scientific Models

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    Implementing Forecasts

    There are two aspects to forecasting implementation, technical and functional. The challenges in both are different, while the technical challenges are easy to solve given the profusion of tools available in the market today, the functional challenges involve significant business process re-engineering and hence are the most typical point where organizations fail to capture the impact of forecasts.

    Technological implementation can be done via modelling tools like SAS, E-views etc. or via forecasting simulators, like TEG’s proprietary FutureWorksTM tool. Given, the forecasting model equation, the tools, would just need the forecasting inputs in order to generate the forecasts. In case of pure time series models, the inputs are simply past figures of the forecasted metric, while in case of causal forecasting models, we need the forecasted values of the input variables as well. This would need multiple models to be created.

    Organisationally, the forecasts need to be essential requirements before taking key decisions on supply chain, future media spend, inventory reallocation etc. It should be in the organisation’s DNA, that any of these decisions will not be taken without a study of how these decisions would impact future demand. Traditionally, this has been the hardest part of implementation, as organizations used to operate in a quick, informal, entrepreneurial culture, often fail to see the benefit of the extra discipline and rigor.

    TEG Scientific Forecasting Process
    TEG follows the CRISP-DM process for all modelling processes, including forecasting.

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    A TEG Case Study
    Situation
    A leading Indian Sports Goods Retailer wanted to develop a scientific forecasting system to foresee the future sales across various product hierarchical levels irrespective of the supply side constraints to facilitate various short & medium-long term business plans. Additionally, the system could provide an early warning of potential slack across chains/stores to enable full resource utilisation course correction.

    Methodology & Results
    After setting up the forecasting objective and scope, a list of potential factors (Fig. 5) were considered to build the forecast model across various channel & SKU-cluster combinations. Rigorous data treatment phase followed and various families of statistical models (specified in Fig. 3) were tested for each channel & SKU-cluster combination. A single model was finalized which produced the accuracy at the satisfactory level. Fig. 6 depicts one such model which was used to produce forecasts for 12 weeks in future. As evident, model is doing a good job in anticipating demand for certain types of interventions like, ICC events & seasonal promotions where demand is supposed to shoot upwards.

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    Key Take-Aways

    The deployment of the Scientific Models to their forecasting process helped the Retailer in the following ways:

    1. Improved Forecasts – The forecasts were improved in the range of ~2-15% across different store-cluster & SKU-cluster combinations.
    2. Better Stock Management – The key achievement was to accurately pinpoint the slack periods for some of the SKU-clusters which were eating up the rack space in those time periods earlier. The retailer was also able to identify the unfulfilled demand for some of the SKU-clusters which was not getting captured with the traditional judgement forecasting approach. Identification of these gaps helped the retailer to better manage the stocks across different store-clusters by relocating them from low demand stores to high demand stores.
    3. Early Warning of Lull Periods – The knowledge of low sales regime well in advance (12-24 weeks) helped the retailer to frame the promotion calendar so that the sales could be hiked up to meet the targets.
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