Zippin through the store

7CEA5DC8-4E5B-4848-B9BC-49A85AE10B85

A Bay Area startup has launched a small public demo in San Francisco that will grow into a full-sized AI-driven convenience store in the coming months.

A Cooler with soft drinks and sandwiches and a shelf full of crisps is just the beginning for this start-up Cashierless C-store concept by Zippin that launched recently. But the Beta concept will scale rapidly Into a fully fledged C-Store as learnings are applied.

The concept uses relatively inexpensive cameras and weight sensors on shelves. The camera feeds are analyzed by algorithms trained through machine learning to recognize the appearance of each product the store carries.to accurately understand when a customer picks up items and puts them in a bag or pocket.

An app is used to sign in and complete the checkout. This also improves the customer experience as customers get used to the technology, and learn how to use it by signing in ( we found that helps customers know that we know they aren’t stealing things ) as well as quick payment.

9674F224-F13B-473B-BC53-DE270CD5E75D

Amazon-Go is rolling out in further stores in San Francisco and New York as well as rumoured to be looking in London. Walmart has just announced a larger less tech but still cashierless store in Dallas.

Zippin will provide another technology that will allow retailers to compete with Amazon-Go in the Better, Simpler, Cheaper stake.

Clever Cars

clever cars 2

What can your driving habits tell us? A lot is the answer. In fact, where people drive can reveal a lot more than Google searches and this is what advertisers, startups, and car-makers are quickly realising.

For years car companies have been installing software and sensors that collect driving behaviour and location data from our cars. This is invaluable to advertisers & car companies alike.

 

Car companies argue this data will enhance the driving experience CX.  It could help to predict flat tires, find parking spaces or charging spots, alert authorities to dangerous crossings & even track criminals fleeing from crime-scenes.

Advertisers are even more excited. Israeli startup, Otonomo, cleans up and organises data for carmakers. They let drivers select the information they’re willing to share with companies in exchange for rewards & discounts – imagine leaving work late and a £5 Dominos discount coming up on your display 🍕

This is only the start. Ford estimates that by 2020 their vehicles will have 100m lines of code and Gartner estimates 98% of new cars in the US & Europe will have an embedded cyber connection.

clever cars

What about BIG data?

The real interesting part is when all this data is aggregated. With all this data, companies can see trends that are linked to other events. For instance:

  1. Hedge funds could use boot sensor data to see how much people bought when they went shopping which would show consumer spending
  2. Banks could see how many people had stopped driving to work, thus suggesting they’ve lost their jobs, and if this number began to rise they could anticipate an economic downturn
  3. 3rd parties could track trips to the police station, domestic violence shelters, STI/HIV testing centres and infer sensitive information about drivers’ health and relationships.

Autonomous cars won’t stop us… 

One of the most important big-picture outcomes here is that car manufacturers are not only hardware companies now, they’re also software companies. It’s often been suggested that traditional companies will die off with the coming of autonomous cars, but this shows they’re using tech themselves to find new sources of revenue.

People need to be aware of the level of privacy they’ll be giving away. Soon your car could know more about you than your family…

Loose Lips cost Lives

strava military

I use Strava to map my runs & cycle rides but a recent article caught my attention on the importance of keeping your data private vs public.

Last November, Strava released a global user-activity heat map showing the running and cycling routes of people wearing fitness trackers. Some of those people work for the military & intelligence agencies.

Their data, which they neglected to opt out of sharing, reveals their daily routines and the contours of previously secret bases for anyone with a Strava account who might be looking. “A modern equivalent of the World War II-era warning that ‘loose lips sink ships,’ writes Jeremy Hsu, “May be ‘FFS don’t share your Fitbit data on duty.’”

So far, the breach hasn’t hurt anyone, and militaries and intelligence services will update their facilities (and personnel training policies) to render this particular vulnerability moot. But the unexpected risks of modern geolocation technology remain. “These digital footprints that echo the real-life steps underscore a greater challenge to governments and ordinary citizens alike,” Hsu writes. “Each person’s connection to online services and personal devices makes it increasingly difficult to keep secrets.”

Read the full article here

https://www.wired.com/story/strava-heat-map-military-bases-fitness-trackers-privacy/

7-11 crawl walk run

7-11 digital transformation using agile crawl- walk-run methodology to develop relevant data driven CX

Data Pulse #711

7-11 seized an opportunity to use the existing technology that most of its shoppers already had in their hands as they entered the store, and it did it from a standing start using AGILE methodology like a baby learning to CRAWL, WALK, RUN

 7-11 can now push real-time, rules-driven offers to customers through the 7-11 app.

The decision was made to launch a mobile app in efforts to deliver what the customer wants, when they want it, where they want it. Offers take account of rich data about the customer, both live and historic:

Real-time transactional: current basket, comms received, channel, geofencing

Real-time contextual: location, location temperature, time of day.

Historic modelling: transaction data, profile data, modelling scores.

Insights gained from feedback to offers over time is incorporated into business rules in a process of continuous refinement.

So, for example, on a cold morning, 7-Eleven might push hot drinks offers. At midday, some customers might receive offers for packaged lunches while others receive promotions on fresh foods. In the evening, lifestyle insights might be used to determine that some customers might be tempted by pizza and a DVD rental.

7-11 2

Dunkin Donuts Data Perks

dunkin donuts coffee and donut

Dunkin Donuts are just beginning to establish themselves in UK but in USA are the largest coffee retailer, and have applied data driven analytics and technology effectively to improve the customer journey.

A coffee and a Donut is one of the most popular calls, and is the mainstay of this convenience foodservice retailer.

Dunkin Donuts recognised the key to convenience retailing lay in the palm of their customers hands and build a customer journey revolving around the smart phone. They created an app based journey where customers could pre-order, collect and pay for their Dunkin Donut. It started with a minimum credible product, simple sign-up and sign-in and has developed into one of the most recognised programmes in USA.

. They understood the customer journey not in part but fully and recognised they were a convenience foodservice retailer and making a coffee and a donut easy for customers would drive more customers to make more visits.

Dunkin Donuts wanted to reward loyal guests in a fast and convenient manner, and provide an overall superior customer experience. Very similar to the goals that Whole Foods had in mind when launching its own loyalty program.

Understanding the Commercial Goals: Dunkin Donuts used advanced analytics to understand the commercial imperatives, and what would best drive them. They recognised that there was a bigger upside from increasing visits and number of visits that slightly increasing the average basket. ( There are only so many coffees and donuts you can eat in one sitting , but it’s important to be the coffee house of choice when there is a choice of 2-3 on the street.

Design a Customer experience that delivers the commercial imperative: They were clearly focusing on driving additional visits from additional customers because they designed a DD Perks programme that rewarded frequency vs average basket.

The Points based reward Rewarded Frequency: Assuming people ordered a coffee and a donut they earned points which became a free coffee every 10-20 visits.  High value to the consumer and relatively low cost to Dunkin Donut.

They also made it easy and intuitive to sign up, and in addition to the basic points structure, Dunkin’ also included features to drive more sign-ups. Sign up on an app downloaded onto their phone,

Make it easy to get to the first reward Customers get a free reward when they join and on their birthday,. That emotional feeling of drinking a free coffee prompts more usage of Dunkin Donut

Make it more rewarding: once the first reward has been claimed targeted offers for incentives and bonus points based on consumer behaviour enable fast rewards accumulation

Make it Easy to Use / Pay Customers must pay with a registered DD payment card at participating locations, or more importantly customers can connect their DD cards to their phone, which enables mobile payments and gets more customers (hopefully) to download the Dunkin’ mobile app.

One last benefit of the program is that customers can share rewards with friends, which is high on many customers’ lists as a desirable loyalty program feature.

Technology developments to make it Easier :  with the onset of Apple Pay, Dunkin Donuts enable mobile ordering through its app. Customers on their way to Dunkin’ Donuts can get their order in quicker, and Dunkin’ can speed up its line. In addition, Dunkin’ also announced interest in Apple Pay as a way to make payments easier for consumers

 

Three key outtakes for success:

  1. Be Clear on the commercial imperative: frequency or average spend
  2. Make it simple, rewarding to use
  3. Integrate across the whole customer experience to make it easy for the customer

Dunkin-Donuts shop

Tech City Coffee

starbucks shop

Understanding customers better has always been critical. Identifying the heart of the commercial challenge and developing customer led solutions to solve them is critical.

Meeting customers needs and simplifying the customer experience using data and digital is a key skill of the new Chief Marketing Officer and delivering the most relevant, inspirational messaging and experiences through advanced segmentation and targeting is a skill every CMO must ensure is delivered.

Starbucks do that

Starbucks carries only 200SKUs but has managed to meet the needs of customers with relevant offers and communications whoever or wherever you are. 

How?

Starbucks Influencing Wheel

Starbucks created a segmentation for customers by day of week, time of day and purchasing details, creating the Starbuck’s Influencing Wheel: which helps frame the problem in terms of what they know about a customer.  Transaction data allows Starbucks to know what behaviours can be observed at purchase time. External f

  1. ENTERPRISE Influences / Transaction data allow Starbucks to know what behaviours can be observed at purchase time ( Food, Beverage, in-store experience etc.)
  2. EXTERNAL Influences ( Weather, Competitors, Events, Community) may impact the way customers behave so Starbucks collected data to simulate local conditions that may affect purchase behaviour.
  3. CUSTOMER Characteristics ( occupation, demographic, need state, day part, media channel preferences etc.)  Not all behaviours can be observed in a transaction so Starbucks deploy .a social listening strategy in order to capture some aspects of a customers lifestyle and how products& services may fit into that lifestyle

starbucks influencing wheel

Customer needs for coffee on way into work, is different to lunchtime or afternoon during the week, and again different to weekend morning coffee. This data is combined with open data to give highly tailored and timely communications with live triggers- offers in the right place at the right time. Arriving at Manchester Piccadilly rail station for early (5-55am)  train to London I get an alert on my phone to pick up a Starbucks coffee for the train. and it really does taste sweet that early in the morning…..

Starbucks also improved the customer experience by being one of the first retailers using a digital app that allows payment through Apple pay or creating a Starbucks wallet that is automatically topped up.

Starbucks are leading the way in delivering the power of value based customer delivery, leveraging data driven analytics and digital technology to drive L4L growth.

Uber focused on data

uber

Uber is a people logistics service that uses a matchmaking model to connect customers directly with drivers to reduce prices for customers by optimising load capacity for drivers. It is now available in 53 countries and more than 200 cities and is revolutionising logistics and service using data. .

The app automatically detects the user’s position using GPS – so ‘riders’ can book a taxi with a single press of a button. Users can get an estimate of their fare by entering their destination. This is calculated by algorithms which consider the distance, prices of similar journeys, and the current Uber price rate.

uber app

Uber uses an algorithmic approach to account for differences in supply and demand in different areas. when supply out-strips demand prices are low, when demand increases the algorithm drives up pricing to encourage more drivers out and optimise revenue. This is called ‘surge pricing’. When demand outstrips supply in a certain area, surge pricing is applied and the usual fare rate will be multiplied appropriately. Users will be notified of surge pricing on booking, and can cancel the trip if they do not want to pay the increased fare.

When a the taxi is booked, a temporary bridge is created between customer and driver data allowing them to make contact and see each other’s location. Once the journey is over and the transaction complete, the exchange of data ends.

Uber scaled rapidly through partnership, using the best experts in any one area ( eg Google Maps, or best checkout system, or best driver id check ) and focused their development on the unique pricing model that optimises pricing to reduce prices for customers, increase occupancy rate for drivers, and drive customer growth and frequency for UBER.

UBER is changing the model for transport in cities around the world, with loyal customers, drivers clamouring to become an UBER driver, and a system determined to continually drive down pricing and increase service levels.

UBER has already changed the way transport works in London, picking up an UBER for shorter and well as longer journeys. replacing the need for a car at all. The future looks good.

 

Blow Up Bedrooms….

lifestyleairbnb

Data Pulse #23

When a few programmers and bloggers bought some air-beds , built a website and offered an air-bed with a coffee on their floor during a particularly busy conference season in San Francisco, they didn’t think they’d be creating a dis intermediation business to rival Marriott or Intercontinental Hotels.

Airbnb is a lodging rental platform with headquarters in San Francisco, California.

airbnb has grown staggeringly quickly over the past half-dozen years. The mind-boggling numbers show its incredible popularity; 1.5million listings in 33,000 cities in 191 countries around the world have attracted 65million guests – and counting.

 

Last June the company was priced at $25.5billion (above hotel giant Marriott’s $20.90bn), and ranked the third most valuable start-up business in the world, behind transportation network company Uber ($51billion), and Xiaomi, the world’s fourth-largest smartphone maker ($46bn).

airbnb has used data to deliver against the brand purpose, tell the brand story and build the customer experience . “Experience the world like a local” 

 

airbnb describes itself as a ‘community marketplace where guests can book spaces from hosts, connecting people who have space to spare with those who are looking for a place to stay.’ A super brand that is community led.

The hosts are business partners, and airbnb is led by what the business partners say, continually getting their opinion and gauging reaction to business challenges and opportunities. It quickly builds a sense of openness, trust and meaningful interacton to form a strong community.

Every year, some 5,000 hosts from more than 100 countries are invited to the company’s airbnb Open (the 2015 edition was held in Paris) and encouraged to talk about the nature of their work. It is a great opportunity to both connect with the hosts and understand how airbnb can help serve them better. It is also a good way to feel part of that broader global community and the local area.

airbnb ran an innovative campaign to engage not only hosts but visitors in the airbnb community. The One Less Stranger campaign – where 100,000 hosts woke up on New Year’s Day, 2015, to an email from airbnb’s founder Brian Chesky saying he had paid $10 into their bank account – was an instance when “full editorial control” was taken away from Airbnb. Brian wrote that we would like the hosts to do something to help someone else, and to meet someone new with that money, It was a $1million marketing campaign where we gave full editorial control to the hosts. Some people just pocketed the money, but the idea here is that you can allow people who are your biggest advocates to be your spokespersons, and do your marketing for you, on social media and word of mouth.

It all builds up to the goal that your brand is driven by community rather than people in a marketing department.

 ‘It’s far better to have 100 people love you than 100,000 people sort of like you.’

airbnb also use data to make a ever growing core of people love them . The platform has disrupted the traditional hotels industry by eliminating the middle man and connecting travellers directly with people who have space to offer. airbnb collects detailed data relating to how customers are using the platform and attributes much of its success to an ability to analyse and understand how to improve the service.

airbnb employs extensive A/B testing to score multiple configurations or designs of its website and apps. Different users will also be exposed to different ranking and recommendation algorithms – the variation they experience is then linked to their next actions – viewing patterns, bookings and ultimately reviews of their stay.

airbnb uses natural language processing to decipher users’ true feelings about their stay, finding this to be more accurate than simple star rankings (which, they hypothesise, are overinflated due to the personal contact between guest and host).

Must admit i was a little nervous using airbnb for the first time ,. Found a little room in deepest Shoreditch, better than the local Premier Inn and cheaper… but now i’m a convert

Data is Magic for Disney

disney Magic band

“advanced technology is indistinguishable from magic”

Data Pulse #7

My god-daughter Rose Bolcato has just visited Disneyland Paris for her Birthday weekend over Easter. she loves the magic that is Disney. Disney  is the place to take your kids ( both small and grown up ones like me). The Disney brand is all about “Magic” and it’s critical to tell that story consistently.

Disney has invested heavily in its new ‘MagicBand’ technology that delivers an enhanced, data-driven experience for guests at Disney World.

The MagicBand, containing an RFID chip and a radio, connects visitors to a network of sensors around the park. The band allows guests to open hotel doors without a key, enter theme parks, use FastPasses for rides, and make purchases without a card.

The only information stored in the band is an identifier – all other data is stored remotely in the cloud. The MagicBands, sensors and supporting systems generate a rich stream of live data: who is visiting which parks, which routes they use, which rides they are visit, when they visit, queue lengths, food purchased, meal times, shows attended, gifts bought, bathroom stops, time spent in hotel rooms and more. This information allows Disney’s analytics team to make data-driven decisions to optimise the park experience so that visitors have a longer, more enjoyable stay – and spend more while they are there.

 

how to make a c-store more Convenient

 

 7-11

Data Pulse #711

7-11 seized an opportunity to use the existing technology that most of its shoppers already had in their hands as they entered the store, and it did it from a standing start using AGILE methodology like a baby learning to CRAWL, WALK, RUN

 7-11 can now push real-time, rules-driven offers to customers through the 7-11 app.

The decision was made to launch a mobile app in efforts to deliver what the customer wants, when they want it, where they want it. Offers take account of rich data about the customer, both live and historic:

Real-time transactional: current basket, comms received, channel, geofencing

Real-time contextual: location, location temperature, time of day.

Historic modelling: transaction data, profile data, modelling scores.

Insights gained from feedback to offers over time is incorporated into business rules in a process of continuous refinement.

So, for example, on a cold morning, 7-Eleven might push hot drinks offers. At midday, some customers might receive offers for packaged lunches while others receive promotions on fresh foods. In the evening, lifestyle insights might be used to determine that some customers might be tempted by pizza and a DVD rental.

7-11 2

To build this capability, 7-Eleven is implementing a Crawl, Walk, Run process:  essentially an AGILE approach to building a customer and data-led convenience store customer experience

  1. Crawl: build the customer database, launch mobile app, introduce offers.
  2. Walk: integrate self-reported data into profiles, feed segmentation and modeling into communication strategies, increase volume of membership, transactions and offers.
  3. Run: launch programmatic loyalty (moving from offers to earned rewards), incorporate unstructured social/web data, advanced analytics, customer engagement.

7-11 3

The app also features an Idea Hub, where engaged customers can offer suggestions for ways 7-Eleven might improve stores, the app, or any other part of its offer.

 

Starbucks data driven mobile approach

starbucks

Starbucks have adopted a data driven mobile first approach to making the customer journey simpler and easier in its coffee shops world-wide. 

Innovating and transforming the Customer experience by leveraging data-driven analytics and technology is critical for success in a 21st Century convenient foodservice retailer. 21% of Starbucks transactions are now completed via mobile … in store at the till using Apple Pay via app or using Starbucks Mobile Order and Pay . What’s more is Starbucks processes more than 6million Mobile Order and Pay transactions a month globally.

Mobile Order & Pay is available on iOS and Android . It’s a relatively new feature of the popular Starbucks mobile app that allows customers to place and pay for an order in advance of their visits and pick it up at a participating Starbucks location. Following successful launches in select US cities , mobile ordering is emerging as the fastest and easiest way for Starbucks customers to order ahead , then pay and pick up their purchases, providing on-the-go customers a simple and quick alternative to get their favourite coffee. Massive in USA and beginning to be trialled in UK,There’s a trial store on Tottenham Court Road.

The Mobile Order and Pay feature allows customers to choose a store from a (Google) map view , browse , select and customise drinks, view the estimated time the order will be ready and pre-pay the order. All within the Starbucks app, and integrated into the existing Starbucks app, and my-Starbucks Rewards loyalty programme. A simple easy way to sign up and earn Stars

Starbucks are leading the way as Tech leaders in convenience foodservice, using data and technology in a way that McDonald’s , Burger King and Dunkin’ Donuts will need to respond to rapidly if they want to respond to customer needs.

 

 

Data Driven Donald Storms Ahead?

trump

data pulse #29

Donald Trump has been quietly building a data juggernaut like Barack Obama: defying sceptics he’s been putting tools in place to get out the vote and results from the states so far suggest he is pulling ahead as front-runner because he’s getting the vote out for his supporters.

In 2015 his campaign assembled an experienced data team to build sophisticated models to transform fervour into votes. The team is led by two low-profile former data strategists, Matt Braynard and Witold Chrabaszcz, and they are using the Republican Data Centre plus supercharging it.

The RNC Data Center 2016 is a powerful query and data management tool, providing an interface for over 20 years of voter contact data. This allows Republicans / Trump to read and write data to and from the platform, continuously serving up the latest information to Trump. If Trump wanted to find 10 people on a residential block that haven’t voted in the past 20 years, have strong views on conservative topics and don’t like the Affordable Care Act, they could do so in seconds.

The system is described as the ‘centrepiece of the RNC’s new data-driven political ground game.’ Voter scoring is employed to track each individual in for contact (by mail, door, phone) and whether they voted (by absentee or on Election Day). Advanced voter profiling even matches social data to voter data automatically

The data push is focused on integrating information Trump has collected, through his campaign website and at voter rallies, on nontraditional or unregistered supporters. It also includes commercial data obtained from the RNC and other sources, in an effort to mobilize voters in key early states.

A data driven Trump seems to be storming the Primaries (he won over 50% of vote in Connecticut, Delaware, Maryland, Pennsylvania, and Rhode Island  with record turnout ) and into the White House in November.

Check out other Blogs : Data Driven Rednecks, Obama in Subscription Business

Data driven Red-Necks

donaldtrump 2

data pulse #28

The Republican Party after the second Obama 2012 election loss, fully reviewed and applied learnings ( and threw millions of dollars at it) They  have developed a sophisticated tool that any Republican candidate even Donald Trump and Sarah Palin can use, focused on the one strategic commercial imperative: winning elections

Republican Party created a permanent resourced Data Centre, with capability developed to support Republican Party candidates in county, state, senate and presidential elections. The RNC Data Centre 2016 is a powerful query and data management tool, providing an interface for over 20 years of voter contact data. This allows Republicans to read and write data to and from the platform, continuously serving up the latest information to GOP candidates across the country. If a candidate wanted to find 10 people on a residential block that haven’t voted in the past 20 years, have strong views on conservative topics and don’t like the Affordable Care Act, they could do so in seconds.

The system is described as the ‘centrepiece of the RNC’s new data-driven political ground game.’ Voter scoring is employed to track each individual in for contact (by mail, door, phone) and whether they voted (by absentee or on Election Day). Advanced voter profiling even matches social data to voter data automatically –

“We don’t just have the information they’re tweeting about, we can match them with their voting and purchasing habits in order to target them through email and social networks” said Jesse Kamzol, RNC Chief Data Officer.

Using powerful predictive analytics, the RNC claim that the Data Centre can tell a candidate, voter by voter, whether each individual is going to vote for them or not, and give reasons for each. At the macro level, this means they can identify how many votes up/down they are, which demographics to target, and then suggest suitable communications strategies to reach them.

This represents a long term strategic play for the Republicans: A permanent strategic and operational capability focused on a clear vision and goal and the right systems / processes /  people continually learning and reapplying lessons learnt. This was first piloted in 2014 successful senate elections where they won majority in both houses and is now being deployed for 2016 senate and presidential elections.

This demonstrates building long term capability in data can have big impacts… we’ll have to wait and see if the investment pays off in November 2016

 

Data driven vision for Social Security in Bolanzo

 

data pulse #32

SMART sensors keep Italian seniors living at home

There have been significant step changes in Healthcare in the last few years through their use of predictive and algorithmic data , data segmentation and technology to solve organisational problems

Limited budgets and resources posed a challenge for the city of Bolanzo, with elderly citizens representing almost a quarter of the population and nearly 50% of social budget. With ongoing medical advances, greater numbers of the elderly are living longer and staying in their homes, often alone. The city wanted to ensure their safety and provide the required services, but needed a cost-effective way to know when people needed help.

bolzano 2

The city has implemented an advanced mesh-network of sensors that monitor the home environment – temperature, CO2, water leaks etc – of elderly citizens living alone. Remote interaction with medical professionals via touchscreen and mobile devices provides healthcare advice, saving trips to the doctor.

The technology will also alert ‘angels’ – friends or relatives of the user – if there is a problem, so they can provide assistance until the appropriate services arrive.

This enables social service and health staff to concentrate on people who really need a physical presence with them, while maintaining excellent quality of life for those in the monitoring programme.

If you’d like to checkout a short film that talks it through, here’s the link through to youtube

3 Single Men and a Wedding

wedpics founders

data pulse #71

First problem: They were guys. Second problem: They were all single. None of them had ever been married, and they were based in Raleigh, North Carolina, which was not a place that anyone had ever built a large consumer app company. Lastly they were entering a crowded market.

How did WedPics defy conventional wisdom?

Identify a small niche market and build a highly targeted product to fit that market’s need. After conquering that niche market, expand out to other niches until you get some real momentum.

Wedpics decided to target brides as their customer niche. Brides can’t get enough pictures and videos, not just from their wedding photographer but also from everyone else with a cell phone.

If this mantra sounds familiar, it should. Think Airbnb, Strava

When you look at WedPic’s success, there are two overriding lessons that stand out:

1.The product is simple. The UI is intuitive, and regardless of whether you are 12 or 62, if you have a phone you can snap pictures and videos and instantly share them with the rest of the wedding party. In fact, the founders claim that there are no features.

2.They understand their buyer (the bride). Out of the gates, they offered instant support regardless of the time of day. The boys each took turns on call and averaged 20 minutes to turn around any question or concern. I mean, we are talking brides here preparing for the biggest day of their life. It didn’t matter that Koren, Heymann, or Miller received calls in the middle of the night. Yes, I said calls. Not email. Not chat (though that would have been cheaper and easier). The brand said, The company is here for you; we know we only get one shot to do this right.

What I love about the success of WedPics is how Koren, Heymann, and Miller  focused on finding customers. My favourite image is that of a six-foot-four tattooed guy (Miller) standing behind the company table at a wedding conference. Today, WedPics stands at 29 people and has raised more than $7.5 million from investors across the country.

Oh, all three founders are still single

Data driven Aussies

ARFU fans.jpg

Players always say that when it counts the most, fans and supporters can represent the force of an extra player in the team. the “Unfair Advantage”

Competing against three other football codes in one of the most crowded sporting markets in the world, the Australian Rugby Union (ARU) has used data to harness that Unfair Advantage.

Data has become a crucial battleground at Rugby’s top level with every aspect of a match and a player’s performance analysed over and over to find that competitive edge. The Australian Rugby Union also use data to create a community at grass roots playing and grass roots supporters. They learnt from the 2012 Olympics in London and went back home and built a bottom up connected supporter community.  

As Bill Pulver, ARU CEO, states:   “The fundamentals of running a sport are pretty similar to running a business, the difference being you have this thick layer of passion over the top. “

Unlocking Fan Force with Rugby Link

Rugby Link is the platform through which the ARU engages every member of its community – “from the age of 5 right through to the age of 75”.

According to Bill, the Australian Rugby Union have a digital platform for omnichannel one-on-one communication on  that is relevant to a fan or player’s historical engagement with the game; as well as enabling a customised future with the game as well.  

A good example that Jade McAuslan, CRM Manager, describes is how the platform allows the ARU to understand when fans and players renew their seasonal membership, and as a result, time and personalise their outreach accordingly. The way that the data and metrics inform that personalised connection is critical to keeping people more engaged than with any of the other codes.

In the modern, professional era of Rugby Union, teamwork and communication are fundamental to success on the field. But also increasingly it is the cohesion and strength  of the entire organisation behind that team, all the way down to the 5 year old touch rugby player, that provides that edge to keep the national teams winning again and again. With Rugby Link,  the ARU has secured an Unfair Advantage in that international contest.

It can help them get to the final… but didn’t stop them against the mighty All Blacks

cutting the corners using data

UPS 1.png

data pulse #22

UPS generates rich data through devices, vehicles, tracking materials and sensors throughout its operations. Using advanced data analytics it aims to ‘turn that complex universe of data into business intelligence’.

Route optimisation delivers immense value for UPS – a reduction of one mile per driver per day results in up to $50 million each year. Telematics sensors in UPS vehicles monitor speed, direction, braking, RPM, oil pressure, shifting, idle time, seatbelt use, and hundreds of other data points, including geographic and map data. The analytics team now runs advanced algorithms to crunch all of this information, factoring in delivery routes, customer information, business rules and employee work rules. These algorithms can determine the vehicle’s performance and condition, and can even recommend driving adjustments.

Through these analytics, UPS reduced total miles driven per year by 85 million. Idle engine time was also reduced by 10 million minutes. The information UPS receives allows fully informed decisions about vehicle replacement, and helps determine best driving practice so that drivers get the best possible training.

“We don’t look at initiatives as ‘analytics projects,’ we look at them as business projects. Our goal is to make business processes methods, procedures and analytics all one and the same.” – UPS Senior Director of Process Management.

 

how to connect customers lives?

#data pulse 17

Times have changed significantly since we built the first unified view of customers in Tesco in 2005 working with Clive Humby and the team at dunnhumby. We were cutting edge at the time and had to build all the systems and processes from scratch. Technology has improved with the likes of IBM Infosphere providing MDM systems , and new agile , start-up mentality ways of working changing delivery timetables.

Gone are the days of 12-24 month development programmes, with waterfall methodology and timescales that mean the business imperatives have moved on before they can be addressed.

Hello to technology and ways of thinking about data problems for the 21st Century where leaders can focus on the business problems and organisational process changes required to solve the problems for customers.

met life 2

The MetLife Wall is a good example in financial services of creating a unified view. It joins all linked customer information in a single place, with one screen that gives all of the information needed to serve customers quickly and effectively. The Wall provides a simple 360 view of each customer across MetLife’s businesses. The interface shows interactions across all touch points (e.g. call centre, in-person interactions with agents, claims, policy updates), connecting more than 70 legacy systems. The application allows customer service agents to reach the information they need with far fewer clicks, and makes it much easier to effectively cross-sell.

The first prototype of the system took just 2 weeks to build. The entire development process, from conception to final product, took just 3 months.

The lessons to learn from Met Life are

  1. a clear focus on what problem you want to solve with the Unified View of Customers.
  2. an agile, start-up mentality where you build minimum credible product and then continually improve.
  3. unified view of customers is a start point to solve business problems not an end point,

AddUp data in Grass roots activation

 sierra club

data pulse#21

Sierra Club is the USA’s largest grassroots environmental organisation, operating at national and chapter level, with more than 2 million members.

AddUp is Sierra Club’s digital platform, combining grassroots campaigning with the power of big data, predictive recommendations and integrated social sharing to encourage activism and demonstrate collective impact. It is a tool to move members through the defined ‘’engagement ladder’ to deliver the organisational commercial imperatives .

sierra club 2

The AddUp homepage shows petitions to sign, social media actions to take, events to attend and ways to recruit friends. With AddUp it is easy to see the difference that each action makes over time. Through real-time updates, campaigners can see how their involvement is driving the cause forward incrementally, and the chain reaction that follows.

siera club 1

E.g. Thanks for engaging to support Dolphins in Florida. 155,000 people supported the petition (2550 in your state) and we forced the governor to change his mind about introducing a new law on fishing that would have destroyed Dolphin environment. Thank you. Would you now like to support this cause: stop introduction of pesticides that will kill Bees.

A predictive recommendation engine suggests campaigns to each user based on what is trending, location, personal interests and, most importantly, previous actions. By integrating across platforms, Sierra Club can track online and offline donations, petition signatures, membership, household groupings, account management, and participation in Sierra Club outings, pulling all of this data together to build a complete view of each member – allowing the user to see their cumulative impact, and Sierra Club to deliver more relevant, targeted comms.

 

Obama- in the subscription business

obama

data Pulse #27

The Obama presidential campaign 2008/2012 created a step change in how data was used in political campaigning. Obama campaign mobilised  a team of experts to give him an unfair advantage against the other candidates. It could be seen as very sophisticated but in reality they were just applying all the simple skills that data-driven Direct Marketers have been using for decades, but doing it with discipline, at scale, and speed.

Obama’s team focused on making sure that Democrats voters would come out to vote on election day, and built a machine that enabled people to be involved and donate their money, time and energy to the cause.

Obama campaign was in “the subscription business”

Obama had clear purpose, but also ran a political campaign in a professional business like manner. he had clear data-driven targets, reviewed daily. Obama built an excellent customer experience that micro-listened so they could target with precision to maximise the impact of communications. They built an engagement ladder to move through in a monitored way from aware, to supporting to donating or actively volunteering.

All messaging was A/B tested to maximise the effectiveness of targeting for different groups. They know that an email from Michelle Obama would appeal more to one group, or from a local Oklahoma name in Oklahoma would work better for a different group.

This enable mass fund raising and mass engagement of grass-roots supporters, and blew away the Republican Party candidates twice.

The rest is history…. until 2016.