Predictive Analytics Innovation Summit 2014 London: Day 1 Highlights
Highlights from the presentations by Predictive Analytics leaders from eBay, Skype, Yahoo and AbsolutData on day 1 of Predictive Analytics Innovation Summit 2014 in London, UK.
While data analytics and modeling can offer an accurate picture of what is happening now, predictive analytics gives an extra benefit in bringing together information to offer an accurate prediction of future action. Effectively knowing how customers or markets will behave before they do offers a new opportunity for companies, and if they are able to capitalize on this before competitors then they will gain a crucial advantage in driving success.
The Predictive Analytics Innovation Summit (May 14 & 15, 2014) was organized by the Innovation Enterprise at London, UK. The summit brought together Analytics leaders from various industries for interactive sessions and thought-provoking discussion on how they are using Analytics to gain a clearer picture of their customers, market and organisation as a whole.
We provide here a summary of selected talks along with the key takeaways.
Here are highlights from Day 1 (Tuesday, May 14, 2014):
Davide Cervellin, Head of EU Analytics, eBay delivered an insightful talk on "Leveraging on Inventory Insights to Support Merchant Growth". In the last 4 years eBay evolved its strategy, leveraging on its strengths to move from being a pure “auction site” to being the place where buyers can find things they need and love. In order to achieve this ambitious goal eBay started changing the core of its user experience to create an environment in which large merchants can supply their products and, taking advantage of a uniquely big and diversified audience, create a solid sales channel. He described the role analytics played in this pivotal change, as well as how analytics are helping merchants compete and win in this scenario.
He explained how eBay is supporting increasingly complex shopper journeys, which he broke down into five phases: awareness, research, purchase, fulfillment, and loyalty. He described eBay's progress in the past through the following three phases: Pioneering Phase (1995-2000, focused on C2c, predominantly auction); Rapid Growth Phase (2000-2005, growth of B2C, online payments); and Retail Phase (2005-2012, category focused, many big retail brands using ebay platform). The next phase is Strategic Partnerships (2012-2015, delivering online/offline value, support with fulfillment/payment and loyalty).
Talking about the merchant development efforts, he mentioned that eBay partners with merchants on each step of customer's journey to deliver consumer insights and support business expansion. Analytics builds a forecast based on all we know about sales in a particular category, going down to day-level. Then, we create reports to track progress and help the business investigate any deviation from the target. Towards the end of his presentation, he walked through a few examples to demonstrate how Analytics has helped in acquisition analysis (assessing brand/manufacturer value and identifying the right market, right price).
Yuval Dvir, Global Head of Business Transformation, Skype shared his experience and learning in his presentation "Data Calling: Transforming an Organisation into an Insight - Driven Business". Data, Analytics and Technology are just the start of a transformation into an insight driven and user centric organization. Addressing the elements of decision making, the integration with Business Strategy, Organisational structure, processes and tools issues, and the obvious cultural change is a complex effort. That complexity increases when you add to the mix a major acquisition, a significant enterprise re-organization, leadership changes and new competition in your category. Skype's small strategic team has facilitated this transformation in a company with over 300 million customers in 200 countries.
Though the idea of transforming to data-driven attitude originated in the product team, soon it was realized that this transformation is required across the organization in order to boost innovation, design strategy and improve execution. Defining strategy as the process of moving from current state to the desired state, he mentioned that most organizations have a good understanding of the desired target state; however, they do not understand their current capabilities and challenges well.
Big Data is not just a technical problem, rather it is a human problem, as deriving real benefits from Big Data needs organizational transformation, which includes dealing with feelings, behaviors, mindsets, motivation, etc. Thus, culture is one of the most important levers for Big Data transformation of an organization. To make Big Data initiative work for Skype was like a big puzzle with several components such as culture, strategy, politics, and capabilities assessment. It was important to do the proper due diligence and get the buy-in from the highest level. It is equally important to customize Big Data to align with firm's business strategy. Big Data needs executive support in order to ensure that it does not gets treated by the organization like just another IT initiative. Big Data initiative is also about Branding and Marketing both within the organization as well as outside.
Big Data and Analytics cannot live on its own. It needs supporting business infrastructure and transparency-supporting attitude to execute actions based on analytical insights. Big Data is not merely about saving time or streamlining operations. When applied correctly, it can lead to real measurable business benefits on key metrics.
Mounia Lalmas, Principal Research Scientist, Yahoo gave a very interesting talk on "To Be or Not be Engaged: What is the Question (to Ask)?". In the online world, user engagement refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. User engagement is a complex phenomenon that requires a number of approaches for its measurement: we can ask the user about their experience through questionnaires, we can observe where they look or move the mouse, and we can calculate various web analytic metrics.
In addition to utilitarian factors, such as usability, we must consider the hedonic and experiential factors of interacting with technology, such as fun, fulfillment, play, and user engagement. Large scale measurement of user engagement can be divided into two categories - Intra-session engagement, which measures success in attracting user to remain on site for as long as possible; and Inter-session engagement, which is measured by observing lifetime user value. The former is focused on user activity, whereas the latter is focused on loyalty and popularity. User engagement varies widely based on the nature of online site, such as gaming, search, social media, news, etc. It is important to not just measure user engagement, but also to interpret it well, because sustainable value is obtained only from meaningful customer engagement.
User engagement is the quality of user experience that emphasizes the phenomena associated with wanting to use a technological resource longer and frequently. It is an emotional, cognitive and behavioral connection that exists, between a user and a technological resource. The measurement of user engagement can be self-reported, cognition-based or interactive. User interest in web page content is a good predictor of focused attention, which itself is a good predictor of positive affect. In the end, she noted that no one measurement is perfect or complete. Measurement should be applied consistently with attention to reliability.
Kulpreet Singh, Senior Vice President, AbsolutData talked about Analytics over the mobile platform in his talk "Mobile Analytics - Decisions Driven by Personal Mobile Data Usage". The mobile is central to our lives in so many different ways. The data that are generated as a result of its usage tells a story about the shape and pattern of our days. This data can provide the opportunity to understand with detail and precision where, when and how tech companies, service providers and brand marketers can most powerfully engage.
The rise in the popularity of mobile platform has created several dilemma for marketers around targeting, timing, channel, message, and spend optimization. The key challenges in using mobile data are its massive data size, unstructured app data and rapidly changing mobile browsing behavior of users. He explained how analytics can guide the process of identifying patterns, helping structure diverse and unstructured data sets and build frameworks that allow organisations to use them to address the real life marketing challenge of how to best leverage mobile to connect with consumers.
Highlights from day 2.