KPIs from Web Agents for Policies Impact Analysis and Products Brand Assessment
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1 KPIs from Web Agents for Policies Impact Analysis and Products Brand Assessment CISIM th International Conference on Computer Information Systems & Industrial Management Applications Agent-Based Computing Antonio Candiello Agostino Cortesi DAIS Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca Foscari di Venezia KPIs from Web Agents 1
2 Summary 1. A PDCA Model for egovernment the approach the egovernment Intelligence framework the software modules 2. Technology & Maps webbots, data scrapers, spiders the open source SpagoBI engine accumulating data in DBs 3. Ongoing Research: ICT-related gender gap KPIs KPIs from Web Agents 2
3 Main Goals Explicit a comprehensive model for: the management of Enterprises marketing initiatives or PAs innovation projects, their systematic monitoring and related impact analysis measurement supported by an integrated Web Agents Intelligence Information System capable of: registering and monitoring such policies, monitor the relative initiatives/projects against their goals, systematically evaluating their impact, reviewing the policies themselves. KPIs from Web Agents 3
4 PAs and Enterprises The information system developed within this applied research provides a continuous source for: Public administrators, that have the capability to continuously improve their services via an objective evaluation of the resulting impact, keeping their citizens better informed and up-to-date regarding goals set in advance for the policies and the success rate of the local government funded innovation initiatives carried out for the public benefit. Enterprise managers, on the other hand, are able to evaluate the effective appreciation of their products/brands by the consumers; can inspect specific attributes that customers associate to the products (like performance, durability, comfort). KPIs from Web Agents 4
5 Sub-objectives a) finding an objective validation for the effectiveness of effectiveness of PAs / Enterprises policies, b) qualifying and quantifying the effectiveness through appropriate impact statistical territorial indicators, c) gathering the relevant indicators via automatic webbots/scrapers and semiautomatic extractors/wrappers, completing the data when needed with focused survey campaigns, d) representing and mapping the indicators showing the explicit relation with the affecting innovation projects and the areas involved. KPIs from Web Agents 5
6 Classes of indicators direct innovation indicators, mainly the ICT indicators enabling the Information Society indirect socio-economical indicators related to the resultant impact of the innovation over the society specific product- and brand-related indicators able to report the evidence of consumers positive or negative opinions regarding specific products or lines of products. KPIs from Web Agents 6
7 The Approach 1. Adapt the PDCA improvement cycle for Enterprises marketing initiatives / PA innovation projects 2. Harvest the Raw Web with webbots, data scrapers, crawlers, spiders, less reliable but frequently updated 3. Use validated official data from Public Authorities and Official Institutions via the Deep Web (online DBs) 4. In-progress research: use the Linked Data of semantic web, access the Open Government Data 5. Geo-refentiate the data to the municipalities level 6. Store the KPI obtained every day (sort of KPI Wayback Machine ) KPIs from Web Agents 7
8 A PDCA Model for Enterprises mkt initiatives & PA innovation projects Policy Manager Plan: define local policies, select innovation KPIs, set goals. ACT PLAN Web Agents Intelligence Do: run projects, begin measurements, monitor KPIs. DO Event Scheduler Agents Manager Policy Act: review Manager policies, redefine goals. CHECK Check: analyse indicators, verify goal achievements, view georeferentiate data. Map Viewer KPIs from Web Agents 8
9 to define policies, projects, actions and to set goals Policy Manager to modify the policies & projects ok ko Policy Manager Plan: define local policies, select innovation KPIs, set goals. ACT KPIs Act: review policies, redefine goals. PLAN CHECK Map Viewer Targets Do: run projects, begin measurements, monitor KPIs. DO HF channel high frequency Check:: verify goal achievements, view geo-referentiated data. to timely wakeup the daemons Events Scheduler Agents Manager LF channel low frequency to manage webbots, adapters & data scrapers to visualize the data Dipartimento via maps & di Scienze tables Ambientali, Informatica KPIs e KPIs dbfrom Web Agents 9
10 Web Intelligence for Enterprises mkt initiatives & PAs innovation projects Plan Do Act Check Policy Management Web Agents Intelligence Definition Policies for Marketing or Innovation Targeting Choice of KPIs & Goals / Thresholds Intervention Start of Marketing Initiatives / Innovation Projects Monitoring KPIs staggered measurement Verification Innovation Impact Analysis & Product Brand Assessment Analysis Geo-Mapping & Statistical Analysis Assessment Review of Policies Evaluation Identification of Critical Points KPIs from Web Agents 10
11 Definition Policies for Marketing or Innovation Targeting Choice of KPIs & Goals / Thresholds Policies Plan Plan Impacted Territorial KPIs Do Check Act Targets Projects Time Milestones KPI Checks KPIs from Web Agents 11
12 Intervention Start of Marketing Initiatives / Innovation Projects Monitoring KPIs staggered measurement Plan Do Do Check Indicatore #1 Indicatore #1 survey #1 Act online DBs data adapters spiders & webbots raw web survey campaigns sources LF channel low frequency direct feed HF channel high frequency indirect feed keywords staggered data consolidation citizens feedback on ICT use & services surveys on ICT KPI Database KPIs from Web Agents 12
13 Verification Innovation Impact Analysis & Product Brand Assessment Analysis Plan Geo-Mapping & Statistical Analysis Web Extractors Low Frequency (LF), High Reliability Channel Check Do Webbots & Scrapers High Frequency (HF), Low Reliability Channel Check Act Surveys Highly Focused but Costly, Medium Reliability Channel Semi-automatic Automatic Manual / partly web assisted geo-visualization layer model business intelligence KPI dbms KPIs from Web Agents 13
14 Assessment Review of Policies Evaluation Identification of Critical Points Plan Act Policies Review Do Check Act Ok Ko change KPIs from Web Agents 14
15 Software Modules Policy Manager [PLAN], to input/define policies, projects, indicators, targets, Events Scheduler [DO], for the reliable planning of monitoring events with (day as minimal temporal unit), Agents Manager [DO], managing webbots, adapters & scrapers, Map Viewer [CHECK], to visualize the data via maps & tables via SpagoBI, KPIs from Web Agents 15
![as minimal temporal unit), Agents Manager [DO], managing webbots, adapters & scrapers,](/docs-images/42/2490149/images/page_15.jpg "Map Viewer [CHECK], to visualize the data via maps & tables via SpagoBI, KPIs from Web")
16 KPIs from data sources and their representation Currently: Official Data collected via adapters/extractors or online webbots. High quality data. Raw Data collected via webbots/scrapers/spiders on the web (the easiest: # of results of searches). Low quality data. In the next future: Open Government Data (egovernment), Open Linked Data (semantic web) also: Local Government could also collect data from different (& raw) web data sources, validate data and expose. Needed data quality assurance activity Data is accumulated daily/weekly/monthly in a DB (twitter scraping would need higher frequencies) Geo-referentiation of data: at regional, provincial, municipality level KPIs from Web Agents 16
17 Advantages of technology used Java (also used PHP and Python) for data daemons (webbots/scrapers/adapters) specific webbots/scrapers for each social or business network/community; needed elaborations of data retrieved. Data updated daily. Host sites could discourage access to web robots difficult to maintain specific adapters for each Institutional online or offline (e.g. csv files) data source. Eurostat offers a wide set of formats & access modalities. However: data updated yearly or monthly easy to maintain SpagoBI to represent and interact with the data on the maps Public Administrators and Enterprise Managers appreciates transposition of data on maps Patterns of effectiveness of policies are clearly visible Postgres DB to deposit the data Accumulating data makes possible to analyse the growth trends KPIs from Web Agents 17
18 Accessing the big Web Repositories Youtube used the Youtube APIs for extracting KPIs on video production & consume (#uploads, #views) also: web parsing youtube site Facebook: not using Facebook APIs (as users have to agree to use the application): we extract data with general web parsing techniques) Google: not using Google Search APIs (limited to max 100 daily searches) using Google Analytics APIs for (owned/managed) web site accesses Limits on number of searches that could be done in a period of time. Yahoo: not using Yahoo! Search APIs (limited to max 100 daily searches) using Yahoo Sites APIs for web sites size and relevance (#pages, #inlinks) Limits on number of searches that could be done in a period of time. General Web: HTML Cleaner APIs for (bad-formed HTML) web parsing (for Java) used Schrenk s webbots library for PHP Blogs and forums are the more interesting sources of information (frequently updated and massive) KPIs from Web Agents 18
19 Mapping the data on the territory: content production & consume Youtube uploads (producers, inhomogeneous) Youtube views (consumers, more homogeneous) KPIs from Web Agents 19
20 Mapping the data on the territory: Work & Education # of ICT Enterprises (work) # of Schools (education) KPIs from Web Agents 20
21 # Mapping the data on the territory: ICT relevance (# of hits) KPIs from Web Agents 21
22 An example for brand assessment Nielsen Brand Association Map (BAM) Adidas Nike Rebook Converse Cool Confort Expensive Googling with the words, e.g. +Adidas +cool KPIs from Web Agents 22
23 Example KPIs (Veneto Region, municipalities > inhabitants) Dati Population peak of uploaded videos Area (kmq) Total Income comune Venezia ,0% Verona ,0% Padova ,0% Vicenza ,0% Treviso ,0% Rovigo ,0% Chioggia ,0% Bassano del Grappa ,0% San Dona di Piave ,0% Schio ,0% Mira ,0% Belluno ,0% Conegliano ,0% Castelfranco Veneto ,0% Villafranca di Verona ,0% Montebelluna ,0% Vittorio Veneto ,0% Mogliano Veneto ,0% Valdagno ,0% Mirano ,0% Spinea ,0% Arzignano ,0% Legnago ,0% Portogruaro ,0% Jesolo ,0% Incomes # of Schools Signs of good community vitality n.video.youtube n.views.youtube n.hits.yahoo n.pages.egov n.inlinks.egov n.ictenterprises %.wide band Bad data (name match) KPIs from Web Agents 23
24 Youtube KPIs: video uploads & views (Belluno province, municipalities) Belluno Feltre Santa Giustina Borca di Cadore Sedico Agordo Cesiomaggiore Domegge di Cadore Pedavena Mel Longarone Sappada Limana Trichiana Selva di Cadore Lozzo di Cadore KPIs from Web Agents 24
25 Population Different KPI layers (Belluno province) Search Engine, ICT-related Income Youtube uploads KPIs from Web Agents 25
26 Thanks for your attention Antonio Candiello, Agostino Cortesi DAIS Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca Foscari di Venezia [email protected] KPIs from Web Agents 26
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