Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Marketing Tech Engine - Meet Magento PL 2015

1,127 views

Published on

Piotr Karwatka's presentation about MarTech at Meet Magento PL 2015

Published in: Marketing
  • Be the first to comment

Marketing Tech Engine - Meet Magento PL 2015

  1. 1. 1 MARTECH ENGINE Marketing Technology Piotr Karwatka
  2. 2. MARTECH – PRACTICAL APPLICATION 2 Client acquisition • Dashboard for monitoring and managing communication in paid media, e.g. Google AdWords, DoubleClick, Google Shopping, affiliate networks, aggregators and price comparison sites, social media; • Centralized media plan; • Aggregation of marketing activities; • Remarketing aggregation; • Aggregation of a client acquisition cost (actual cost); • Combining data from marketing, CRM, call centers and other off-line sources; • Antifraud systems; • A network of dynamic landing pages; • Unified analytics - connecting tools, e.g. Google Analytics, Gemius, CMS. Purchasing retention • Dashboard for monitoring and managing communication with clients in owned media, e.g. e-mail, SMS, push notification; • Marketing automation; • Customer segmentation; • Product recommendations; • Loyalty programs; • Customer scoring (customer assessment and valuation); • Unified analytics - connecting tools e.g. Google Analytics, Gemius, CMS, system marketing automation. Direct sales • Vendor dashboards for managing communication with clients in on-line and off-line media; • Monitoring customer health; • Cross- and up-selling web/marketing mechanisms for use by vendors; • Predefined components for communicating with customers, e.g. everyday brochures ready to send; • Mechanisms of product recommendation; • Mechanisms supporting direct sales, e.g. potential and risk customer alerts. CRO/UX automation • Layout personalization; • Product recommendations; • Search engine personalization; • Navigation personalization; • Management dashboards for website personalization.
  3. 3. MARTECH – LOGICS 3Source: Hybris TOUCHPOINTS SITES, APPS, ADS, E-MAIL, OFF-LINE ACQUISITION RETENTION CRO DIRECT SALES
  4. 4. ACTIONABLE DATA 4 Purchase History CRM Cookies (behaviours on www) GA API Social Media SalesManago Customer’s data: Sales Data e-Commerce / ERP / POS Data Aggregation Algorithms and Logic Big Data + Reco Engine Cloudera Reporting Personalized Communication Dynamic content Marketing Automation: Sales Dashboard
  5. 5. MARTECH OPEN ARCHITECTURE 5
  6. 6. MARTECH OPEN ARCHITECTURE 6
  7. 7. PERSONALIZATION AND/OR MARTECH – DEVELOPING 7 Analysis of shopping habits Prototype of personalization elements Testing personalization prototypes Designing a dedicated MarTech solution Implementation and integration Goal – to detect key purchasing habits, system constraints and develop the concept of solution and project scope. Realization – workshop, input data analysis (database analysis in the areas of trade, product and customer), IT systems analysis; preliminary technical analysis. The effect of work – conclusions from the conducted analyses (used in marketing, sales, IT and UX) MarTech and personalization development plan, a preliminary plan of MarTech and personalization mechanisms application in the organization. Goal – to develop the first version of personalization and Martech components (segmentation mechanisms, recommendation mechanisms, data aggregating and processing mechanisms) along with a plan of their use/ implementation. Realization – creating concept, mockups, developing prototypes of mechanisms operating independently of the current IT system. The effect of work – prototypes of personalization and MarTech mechanisms and a plan for testing them. Goal – to test and optimize personalization and Martech prototypes. Realization – research/testing, optimizing the mechanisms (conceptual work, mockups, developing prototypes of mechanisms operating independently of the current IT system). The effect of work – tested and approved prototypes of personalization and MarTech mechanisms; revised MarTech and personalization development plan. Goal - to design the final version of MarTech and personalization solutions, create mockups, and the implementation backlog. Realization – creating final Axure mockups, preimplementation analytics, The effect of work – Axure mockups, implementation backlog, planned implementation analytics (IT and the mechanism application in the organization). Goal - implementation of personalization and Martech mechanisms, using the gained knowledge in the current sales and marketing activities. Realization - IT implementation carried out under the strict supervision of a MarTech engineer.
  8. 8. VENDOR DASHBOARD – ALERTS BY SEGMENT 8 Source: http://www.slideshare.net/RetentionGrid/your-retention-marketing-todos-for-each-customer-loyalty-segment/ Potential applications: • Detecting customers’ potential by segmentation e.g. frequency of purchase, the time since the last purchase or purchase value; • Preparing and/or automatic delivery of pre-defined e-mail campaigns, e.g. win-back campaigns for new customers who have not got back to a store; • Detecting promising customer segments, working on customers using layers: an increase in purchase frequency, increasing the purchase value, reducing the time since the last purchase.
  9. 9. CRO – PERSONALIZED LAYOUT 9 Potential applications: • Homepage tailored to the customer's profile (blocks, offer, navigation, pop-ups), personalization based on historical data, e.g. a logged in and not logged customer and data from external sources, e.g. Facebook; • Dynamic website elements (blocks, pop-ups) appearing depending on the profile and behavior on the website.
  10. 10. VENDOR DASHBOARD – OFFER BY SEGMENT 10 Source: http://workingperson.com/, http://www.windsorcircle.com/ Potential applications: • Automatic preparation and/or sending an e-mail message containing products and promotions tailored to customer segments or individual customers; • Managing recommendations engine, taking into account the business logic, promotions, inventory and marketing plans.
  11. 11. VENDOR DASHBOARD – MARKETING AUTOMATION 11 Source: http://www.preact.com/, https://rjmetrics.com/resources/reports/ecommerce-buyer-behavior/ Potential applications: • Messages sent automatically to the customer at a pre-planned scenario, e.g. abandoning the ordering process, abandoning a shopping cart, abandoned page (while browsing); • A sequence of messages welcoming and introducing the client (onboarding); • A sequence of messages reactivating or recovering the client; • Dedicated offer of the day/week sent automatically to customers.
  12. 12. VENDOR DASHBOARD 12 Source: https://canopylabs.com/ Potential applications: • Specifying up-selling recommendations (product range, time of transfer recommendations) directly at the level of individual clients; • Specifying preferred format and frequency of contact by the sales department; • Tracking individual user behavior (on-line, off-line); • Detecting clients with increased risk of loosing them.
  13. 13. VENDOR DASHBOARD – UP–SELLING ALERT 13 Source: http://www.preact.com/, https://rjmetrics.com/resources/reports/ecommerce-buyer-behavior/ Potential applications: • Detecting customer segments with similar shopping preferences; • Detecting clients with specific behavior, e.g. impulsive shopping, promotion shopping, purchasing supplemental stocks of a product; • Detecting customers interested with the selected product, product type or kind of promotion/trigger e.g. a discount coupon for free delivery.
  14. 14. VENDOR DASHBOARD – REORDER/REPLENISHMENT ALERT 14 Source: https://www.justrightpetfood.com/ Potential applications: • Detecting the correlation between the next purchase and a specific product (purchase recurrence); • Developing customer segments that are willing to renew stocks of a product; • Automatic preparation and/or sending e-mails convincing customers to repeat the purchase. • Managing the described communication.
  15. 15. ACQUISITION – MONITORING COMPETITION ACTIVITIES 15 Source: Dealavo Potential applications: • Monitoring of prices, offers, promotional campaigns, the scope of marketing activities by competition; daily update of data; alerts.
  16. 16. ACQUISITION – DATA AGGREGATION 16 Source: Hybris Potential applications: • Aggregation and integrating data from multiple sources, e.g. CRM, Call Center, Google Analytics, Marketing Automation system, cash system, marketing tools, etc.; • Managing a single mediaplan and purchasing media from one panel (data integration from internal systems with marketing tools, e.g. AdServer, Marketing Automation, affiliate networks); • Supplementing aggregated data with external data e.g. demographic or social profile, data correctness.
  17. 17. ACQUISITION – FEED MANAGEMENT 17 Source: Lengow Potential applications: • Marketing management based on an offer – emitting product ads (XML); • Promotion management in the context of sponsored links, Google Shopping, price comparison websites, offer aggregators, affiliate networks, dynamic remarketing (product presentation), RTB (product presentation), social media (Facebook Ads, Pinterest), marketplace (Allegro, eBay, Amazon and other ); • Managing pricing and promotions policy from a single panel.
  18. 18. CORRELATION ANALYSIS 18 Potential applications: • Detecting product + product correlation, e.g. most frequently purchased product; • Detecting correlation between customers/users; • Detecting correlation between behavior on the website (visiting specific sites), and purchasing; • Detecting correlations between stimuli/triggers and purchasing e.g. customer response to promotions; • Detecting correlation between th time of purchase and the scale and type of purchased products; • Detecting correlation between repeating purchase.
  19. 19. CORRELATION ANALYSIS 19 Potential applications: • Detecting product + product correlation, e.g. most frequently purchased product; • Detecting correlation between customers/users; • Detecting correlation between behavior on the website (visiting specific sites), and purchasing; • Detecting correlations between stimuli/triggers and purchasing e.g. customer response to promotions; • Detecting correlation between the time of purchase and the scale and type of purchased products; • Detecting correlation between repeating purchase.
  20. 20. 20 EXAMPLE ANALYSIS
  21. 21. PERSONA ANALYSIS 21 Potential applications: • Analysis of shopping habits according to personas defined on the basis of interviews and/or testing, e.g. promotion hunters, gift buyers, thrifty customers, novelty fans, buyers using recommendations, etc. • Analysis of stimuli/triggers in an offer or a marketing strategy stimulating customers to action; • Modeling triggers and a method of communication (range, scope and frequency) broken down by individual personas; • Combining qualitative and quantitative research.
  22. 22. ANALYSIS OF PROBABILITY 22 Potential applications: • Construction and optimization of probability models; • Detecting customers with the highest likelihood of purchase recurrence; • Detecting customers most likely to be lost; • Detecting breakthroughs in building customer loyalty, e.g. „Starting the purchase of product X significantly increases the chance of being loyal" or "after the fifth purchase the customer becomes loyal."
  23. 23. SHOPPING SEQUENCE ANALYSIS 23 Potential applications: • Detecting purchase sequence in the following aspects: product category, product brand, specific product or cart size; • Detecting shopping preferences depending on the order of purchase.
  24. 24. ANALYSIS AND PREDICTION OF CUSTOMER VALUE IN TIME 24 Potential applications: • Analysis and customer segmentation according to customer value in time detecting characteristics common to the most successful clients; • Predicting customer lifetime value (using probability analysis); • Detecting Pareto 20% (the best clients in terms of purchase value) and aspiring segments.
  25. 25. FIRST PURCHASE ANALYSIS 25 Potential applications: • Analysis of marketing activities (traffic sources, media, campaigns, triggers/discounts, season) for generating new customers; • Detecting marketing components responsible for bringing new customers; • Calculating the cost of acquiring a new customer; • Multichannel analysis (taking into account conversion attribution).
  26. 26. TOUCHPOINTS ANALYSIS 26 Potential applications: • Detecting key points of contact with an offer (website, application, landing pages, marketing, off-line); • Modifying UX/marketing so that they lead customers to the appropriate places on a website; • Detecting and removing unwanted elements in UX/marketing.
  27. 27. ANALYSIS AND PREDICTION OF CUSTOMER VALUE IN TIME 27 Potential applications: • Analysis of marketing activities (traffic sources, media, campaigns, triggers/discounts, season) for expected customer value in time, the likelihood of purchase recurrence and the likelihood of becoming a loyal customer; • Detecting marketing components responsible for bringing the most valuable customers.
  28. 28. THANK YOU! QUESTIONS? 28 Piotr Karwatka (pkarwatka@divante.pl) Divante Ltd – http://divante.co

×