Big Data Strategy

Manufacturing and Sales metrics:

Structured Metrics:

ROMI=Increase in sales - Marketing campaign investment) / (Marketing campaign investment) Cost per lead=Total cost of marketing campaign/Number of leads generated Engagement score=Number of leads generated/Number of contacts made. Market share = (Total sales made by Nestle/Total sales made by competitors Existing customer retention ratio=(Sales made to existing customers/Total sales) Brand awareness = (Number of people who know brand Nestle/Number of people surveyed) Brand Fame Index =Perceived Popularity / Actual Popularity.

Unstructured Metrics:

Social media likes on Facebook and other social networking websites. Review and feedback of CSR initiatives through Sustainable Development Society of Nestle

Production Metrics:

Structured Metrics:

Number of new products offered Percentage increase in the sale of adapted products Percentage of products adapted/targeted to the low-income consumer segment order-to-delivery lead time supply chain response time delivery performance (On Time / Delivery Date) Percentage reduction in energy/water usage/waste generated per unit produced/ carbon footprint Percentage products in compliance with consumer product safety and labelling regulation Stock out rate, time-to-market.

Big Data capability

WorkFlow- Capability Maturity Model.

Nestle is now in level 2 of Capability Maturity Model. With increasing number of smart phones and last mile telecom connectivity it becomes imperative for Nestle to collect customer data from remote areas. Data services need to be defined, built, managed and published to provide consistent data based management capabilities.

Roles, Time, How , What?Big Data Champions- Serve as business enabler for new market strategies.Information Architects and Enterprise Architects- Design and support ecosystem consisting of diversified information assets and related schools. Enterprise architects can assist Information Architects in developing a cross-platform design that implements the expanded information architecture capabilities and ensures interaction between applications.Data Scientists- Data Scientists mine data, apply statistical modelling and analysis, interpret the results, and transform data results to application and to prediction.

Level 0 Fragmented---Point solutions, Overlapping content, labor intensive, no governance

Level 1 Managed---Enterprise Data Warehouse, Searchable Metedata repository with business, revenue, financial data in single warehouse, unstructured data not leveraged.

Level 2 Systematic--Standardized enterprise vocabulary across enterprise, customer master data defined and integrated at operational level, additional data sources incorporated to include costing model, supply chain and customer experience

Level 3 Advanced--Automatic reporting with agile production, reduction of waste and customer variability,Customer data from multiple touch points, Monitor opportunities to improve quality, centralized data governance for analytical accuracy

Level 4 Optimized--Actionable analytics to improve operational and risk intervention, quality of products and services maximized, interoperability and service oriented Architecture, quality based compensation based on metrics accuracy.

Level 5 Innovative--Matured and governed prescriptive analysis policies, knowledge sharing and data exploration, enterprise data warehouse updated in real time. best practices identified and documented.

Business Analysts- They help data scientists to understand the business context.Hadoop Administrators- They maintain Hadoop distribution and platforms.

Training and Skills requirementA cross-functional team of analytic and domain specialists who plan and prioritise analytics initiatives, manage and support actions to be taken, and promote large scale use of information and analytics best practices throughout the enterprise is called as Centre of Excellence. The key benefits experienced by Nestle on implementing a COE will be:1)Cross-training of professionals from diverse data science disciplines2) Allocate resources for the benefit of the organization and has the scalability and flexibility to serve enterprise centric initiatives as well as specific business units3)Modify the culture of the organization to appreciate the value of analytics-driven decisions and promote continuous learning.

Application StrategyBig data strategy of Nestle mainly comes under non transactional data type and measurement of business objectives. The following methodologies can be used:

Crowd sourcing:

A process for collecting data from a large community or distributed group of people Idea submission is a common crowdsourcing activity

Textual Analysis:

Computer algorithms that analyze natural language Topics can be extracted from text along with their linkages

Sentiment Analysis:

A form of textual analysis that determines a positive, negative, or neutral reaction Often used in marketing brand campaigns

Network analysis:

A methodology to analyze the relationship among nodes (e.g., people) On social media platforms, it can be used to create the social graph of follower and

friends’ connections among users

Tools Used: Radian6 Attensity Visible Technologies Converseon HootSuite NodeXL network graphs HP Autonomy

Direct Customer relationship- creates direct relationships with consumers. Engaging directly with consumers increase loyalty and provide insights on individual consumer needs, which results in more accurate targeting of products and promotions. Big data helps in maintaining social brand presence, monitoring consumer responses and responding in real time requires a complex set of technology solutions.

Mobile and Location Based services – It ensures connection to retailers and customers whenever required. Emerging technology capabilities turn mobile device to primary personal shopping tool, with which consumers explore, try, buy, and share their experiences with new products. This will lead to reduction in product launching cost. By scanning codes on the product with smartphones, consumers can get further information on the product.

Predictive Analysis – Massive historical point of sales and promotions data is blended with real time data from social media to predict daily demand and optimise promotions to maximize sales and profit.

Demand driven Supply and change Management—Nestle can leverage big data by mapping demand based supply chain system to minimize inventory levels, improve service quality and reduce stock outs. Real time demand can be blended with traditional forecast to develop new IT systems which will enable data sharing with customers and retailers.

Idea to product acceleration—Virtual simulation techniques help to study consumption of products sitting on shelves which in turn leads to faster go to market and at lower cost.

Safety and traceability-- Increasing consumer and regulatory focus on safety and greater likelihood of product recalls, has made it essential to trace product through the supply chain from the raw-goods supplier into the store.

Sustainability—Big data can help gather information on sustainability by tracking, managing and analysing huge amount of data in supply chain.

Hadoop, CRM, Webapps are some examples.

Storage SpaceCustomer purchase related data and ordering data from retailers can be backed up to establish vital pattern.Disaster Recovery Plan needs to be in place to ensure business continuity.