Discovering Customer Love

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We believe that digital marketing is broken. Display advertisements are obtrusive, interruptive and annoying. It's no surprise that ad blockers are becoming the rule rather than the exception. Remarketing strategies are often invasive and misplaced. Content marketing can be compelling but is tainted by the avalanche of click bait. Click-through rates continue to decline and reversing this trend can only be achieved at the expense of customer love. Identifying and nurturing customers who love what a business does has always been a cornerstone for marketing success. Mike's workshop will explore the similarities between trading in the 1980s and marketing today in terms of the deluge of data. He will argue that most digital marketing strategies amount to customer abuse and will present an alternative marketing strategy based on customer advocacy. He will share some insights from our market research and some aggregated insights from clients as well as businesses like AirBnB and Uber.

Business

Mike Adam, Digital Animal CEO
Encouraging genuine recommendations
June 28th 2016

Shout!
Interrupt!
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Pester!Outreachdigital.org @outreachdigit

Stop customer abuse.
Outreachdigital.org @outreachdigit

Discover customer love.
Word of mouth is as old as the hills.
Recommendations are the main influence in more
than 50% of purchase decisions.
Outreachdigital.org @outreachdigit

Closeness
if the recommender and recommendation recipient are family or
friends, the recipients’ propensity to buy doubles.
Expertise
if the recommender moves from ‘minimal expertise’ to ‘expert’, the
recipients’ propensity to buy triples.
Timeliness
if the recommendation is received when the recipient is actively
looking, the recipients’ propensity to buy doubles.
What makes a powerful recommendation?
Outreachdigital.org @outreachdigit

Customers are valuable not just in how much they spend but in how much they
influence others to spend.
Most people don’t share via share buttons; they copy and paste the URL and
send to friends. Most marketers don’t track address bar sharing.
Understanding address bar sharing identifies which content is shareable and
which customers are natural sharers.
Our campaigns show a high incidence of proximate sharing, where the
recommender and recipient are co-located (same office/household).
Things we’ve learnt
Outreachdigital.org @outreachdigit

Do you treat your customers well?
Do you discover customer love?
What is the future of digital marketing?
Outreachdigital.org @outreachdigit

Recommendation based on real product experience

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This document outlines a 7 step process for creating data visualizations in Tableau. It includes an agenda, descriptions of each step, and demos. The 7 steps are: 1) Connecting to data, 2) Cleaning and preparing data, 3) Creating initial visualizations using Show Me or drag and drop, 4) Editing visualizations, 5) Analyzing data and creating additional visualizations, 6) Creating interactive dashboards, and 7) Sharing visualizations. The presenter leads attendees through examples on air pollution data and life expectancy data to demonstrate the process.

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This document provides an overview of Tableau, a business intelligence software for data visualization and analytics. It outlines the 7 key steps to get insights from data quickly using Tableau: 1) connect to a data source, 2) manage the data, 3) create visualizations, 4) edit visualizations, 5) create additional visualizations, 6) build interactive dashboards, and 7) share visualizations. Tableau offers an easy and fast way to transform data into interactive visuals that help users identify patterns and trends to inform business decisions.

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The document discusses using Pandas and Jupyter Notebook for data analysis in Python. It introduces Pandas as a data analysis library for working with tabular data like spreadsheets. The key points covered include: - Using Anaconda distribution for a complete Python data analysis environment - Opening Jupyter Notebook and creating cells to run Python code - Loading data from files into Pandas DataFrames for analysis - Filtering and exploring DataFrames using methods like df[] and Counter() - Examples of projects like gathering SEMRush data and custom tracking charts

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The document outlines the four main steps to search engine optimization (SEO): 1) Research - Understanding keywords, buyers, and competitors through keyword and competitor analysis tools. 2) Technical SEO - Ensuring the website is technically optimized for search engines through DNS settings, XML sitemaps, speed optimization, and more. 3) On-Site SEO - Optimizing individual pages through titles, headers, text, links, and keyword density. Plugins like Yoast can help. 4) Off-Site SEO - Tracking search analytics, links, influencer outreach, and domain authority to improve search rankings over time. Repeating the process is also important.

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Learn how to launch a product in the hospitality space. Find out who's winning and who's losing? Think Deliveroo, Uber Eats, Starbucks and an industry moving more offline to online. Are robot waiters and tablets taking going to take waiters jobs? Personalised loyalty, paying at table, skipping the queues, last minute reservations and all the newest innovations within hospitality. SPEAKER Ben is director at BPL Digital, the leading digital agency for the hospitality industry. BPL work with the largest global brands including Budweiser, Compass Group, Greene King and the global brands of the future including The Goodman Group (Burger & Lobster, Goodman Steak Houses, Zelman Meats and Smack) and Grind & Co.. BPL have been shortlisted for a number of awards including Best Customer Experience and Best Mobile Wallet Innovation at the prestigious Mobile Innovation Awards.

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Find our more way to use marketing for your startup. April Dunford - an engineer by training, April worked as a marketing and sales exec for most of her career. She's run marketing and sales at a number of startups including Tulip Retail, DataMirror (acquired by IBM), Janna Systems (acquired by Siebel Systems), Sitraka (acquired by Quest software), Watcom (acquired by Sybase). She's also been a marketing exec at few larger companies where I’ve focused on launching and growing new product lines including IBM, Nortel and Siebel Systems.

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This document provides an overview of SuperAwesome Ltd, a company that provides insights and solutions for marketing to children and teens in the digital space. It discusses SuperAwesome's large audience reach of 250 million kids and teens online, and their expertise in gaining exclusive insights through surveys of over 30,000 UK members and analysis of user behavior on their platforms. The challenges of marketing to digital kids are outlined, including regulatory restrictions, audience fragmentation, and kids' changing relationships with advertising. Key points are that kids are creators who value purpose and transactional engagement with brands through digital content and platforms.

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This document discusses machine learning applications for digital advertising. It describes how machine learning can optimize advertising by predicting click-through rates and conversion probabilities based on user, ad, and website data. However, obtaining sufficient high-quality data for machine learning models is challenging for advertisers due to data sparsity, high costs, and lack of access to raw user data from platforms. The document also outlines some machine learning techniques like gradient boosting trees that have shown promise for digital advertising optimization.

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An exciting talk on the main difficulties and how to overcome them when building and scaling data teams with Florian Douetteau - Technological issues: What stack should they choose for the company’s architecture? And what about big data technologies; should they accept being a polyglot or rather assume being a ruthless dictator? - HR issues: Who should they hire? Should they build their data team as an extension of the BI team? Or should they build it from scratch? - Data issues: How are they supposed to get data inside his data lake? Which strategy should they adopt: the cicada, the spider or the fox one? - Product issues: What is big data really about? And eventually, what are they willing to do with this bunch of data? The talk aims at demonstrating how tough it can be to build and scale a data department, and at giving some insights about the strategy Florian thinks they should adopt.

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7. Closeness if the recommender and recommendation recipient are family or friends, the recipients’ propensity to buy doubles. Expertise if the recommender moves from ‘minimal expertise’ to ‘expert’, the recipients’ propensity to buy triples. Timeliness if the recommendation is received when the recipient is actively looking, the recipients’ propensity to buy doubles. What makes a powerful recommendation? Outreachdigital.org @outreachdigit

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