Consumer Goods and Retail in The Digital Age ALESIMO MWANGA KOMALIN CHETTY

A Proliferation of Data The Proliferation of Data The basic idea behind BIG DATA is that everything we do leaves a trace (or data), which we and other can use and analyse. BIG DATA refers to our ability to make use of the ever increasing volumes of data Activity Data, Conversation Data, Photo and Video Image Data

Creating Value from BIG DATA  Understanding your customer better  Target Products/Services/Promotions  Churn Models- Proactive customer management  Understand and optimise business processes, eg Supply Chain  Embrace the BIG DATA ECONOMY  Any business not embracing the value of BIG DATA faces the risk of being left behind

Big Data Analytics  Big data analytics is the process of examining large data sets containing a variety of data types -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.  The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits

The Science of BIG DATA  Big data can be analysed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis.advanced analyticspredictive analyticsdata mining text analyticsstatistical analysis  Data Scientist : “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It's almost like a Renaissance individual who really wants to learn and bring change to an organization."  inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure

Big Data Analytics Global or Domestic Trend?

Crucial way for leading companies to outperform their peers Established competitors and new entrants will leverage on data-driven strategies to innovate, compete and capture value In healthcare, data pioneers are analysing outcomes of pharmaceuticals when they are widely prescribed and discover benefits and risks that were not necessarily evident through limited clinical trials Such knowledge then informs the creation of new service offerings and the deign of future products

Big Data Analytics Global or Domestic Trend? Create new growth opportunities Middle – man organisations, information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analysed

The era of Big Data could yield new management principles The real time and high frequency of data is just as important adding considerable power to prediction McDonalds equipped some stores with devices that gather operational data as they track customer interactions, traffic in stores, and ordering patterns. Researchers can model the impact of variations in menus, restaurant designs, and training on productivity and sales

In future, analysts and researchers say companies will routinely monetize their own data for financial gain. Most companies especially online businesses and most recently industrial firms are already rebuilding their strategy around data, however as an asset, data privacy and liability concerns are probably the most important monetization questions businesses need to consider.

We Can Build The Bionic Brain Stronger. Faster. Better

Cognitive Analytics Automative Knowledge Intensive = Technology + Computing Power + Human Interaction

Traditional analytics, data representing complex challenges or questions is analyzed, patterns are identified and historical or predictive insights are generated to inform decision making around those issues. Cognitive analytics goes one step further, feeding learning's back into the analytics ecosystem to be applied to the next iteration and new or related challenges. With each iteration, the mechanism gets smarter. With cognitive computing, these recommendations can also be ranked by how likely it is that the response is accurate. What's more, iterative learning takes place at the machine level. The more data fed into a machine learning system, the better quality insights you get out of it.

Big Data: The Management Revolution Booksellers in physical stores could always track which books sold and which did not. If they had a loyalty program, they could tie some of those purchases to individual customers. Once shopping moved online, the understanding of customers increased dramatically. Online retailers could track not only what customers bought, but also what else they looked at; how they navigated through the site; how much they were influenced by promotions, reviews, and page layouts; and similarities across individuals and groups.

“ Before long, they developed algorithms to predict what books individual customers would like to read next. Traditional retailers simply couldn’t access this kind of information, let alone act on it in a timely manner. It’s no wonder that Amazon has put so many brick-and-mortar bookstores out of business.”

Big Data Innovators