The place where the Customer counts

Free thoughts on (Social) CRM, (Social) Business and the next thing

The place where the Customer counts - Free thoughts on (Social) CRM, (Social) Business and the next thing

Where is your Social Caring team?

Many applications are struggling in the Social CRM market space nowadays and more and more are trying to “verticalize” their features in order to adapt them to the traditional customer-facing processes. This is the case of Customer Operations and specifically of Customer Service approach through well-consolidated processes adopted in contact centres. Since the Social CRM capabilities’ drivers come from Social Listening/Monitoring and traditional CRM activities, there is a consistent effort from vendors coming from the CTI world that are trying to match these two aspects, replicating what’s in place in your contact centres (see image below) and minimizing impacts on processes through the delivery of concepts like “universal queue” (in other words: assembling customer interactions coming from different channels – included social media – and assigning them to CSRs thanks to business rules and constraints).

customer service chain

So the integration between social and traditional approaches is set on the upper side of the Customer Service chain (contact management layer).

This is for sure a reasonable perspective especially for COOs that are really careful to performance and cost issues. However, are we sure that this is the right or at least the most complete perspective to use? People complaining or asking for commercial information are physically the same independently if they use a phone, an email, or other touchpoints. Nevertheless, are we really confident that they also behave in the same manner or (worse) they use those touchpoints with the same expectations?

How many social media gurus have told you that social customer will have an impact on your business through social media because of their characteristics of being “public” and “networked”? Well, do you know what? They are right. And if they are right, it also means that social customer is more careful about its expectations, satisfaction and its power to spread publicly frustration and rage against bad service.

Therefore, COOs have to understand that, in addition to cost issues, they need to cope with social interactions in a more dedicated way. And that’s possible, in my humble opinion, only if you use an organizational perspective which set a dedicated social caring team with specific responsibilities and skills (particularly oriented to the management of the relationship through social media) with their own service levels and business workflows. In other words, I believe that the integration between social and traditional approaches must be executed in the lowest side of the Customer Service chain (operational CRM or case management layer) to allow the final collection of all interaction data in a unique place.

What do you think? Please leave in the comments your point of view.

Good news for/from CustomerKing

Decisyon logo

A really short post just to announce a good personal news. From October 1, I’m very proud to begin a new professional challenge in Decisyon, one of the most interesting enterprise with an outstanding Collaborative Decision Making & Execution (CDME) platform for rapid development and cloud delivery of operational analytics, planning, in-context collaboration and execution applications.

One of its main solutions, which I followed during the last years considering my interest on customer service evolution, is Decisyon/Engage, a social CRM tool strongly focused on social media analytics, social caring and monitoring which help businesses obtain sustainable competitive edge particularly thanks to the integration between customer data collected from outside and inside corporate boundaries.

And that’s the point for the next future of Social Customer Service, in my opinion. The capability to link data coming from different kind of sources in order to better outline and understand your customers from various perspectives, collaboratively find the best way to satisfy their requests and finally activate/execute the right corporate processes to induce mutual and shared value.

This is one of the biggest challenge Decisyion will face in the next years, thanks to the endorsement of important US Venture Capital firms.

This is one of the biggest challenge for the next social CRM phase.

So, good luck to me and see you soon.

Una piccola introduzione ai sistemi di raccomandazione

Il titolo può sembrare fuorviante ma ciò di cui mi voglio occupare in questo post sono i cosiddetti sistemi di raccomandazione alla base del successo di aziende come Amazon o Netflix. Sistemi che tengono traccia delle preferenze esplicite fornite dagli utenti sui prodotti e che, attraverso l’uso di algoritmi di machine learning, forniscono suggerimenti su nuovi prodotti/servizi che potenzialmente sono di interesse per l’utente. Chissà a quanti di voi sarà capitato di rimanere piacevolmente sorpresi nel constatare che uno specifico suggerimento vi ha indotto a comprare un nuovo prodotto come se qualcuno vi “leggesse nel pensiero”, indovinando i vostri gusti e le vostre preferenze. Eppure dietro a tutto questo non c’è nulla di magico, ma solo l’uso di sofisticate tecniche statistiche che consentono di analizzare approfonditamente le scelte effettuate in passato (acquisto, recensione, voto, ecc.) e le correlazioni tra comportamenti di utenti diversi, con il fine di scovare “affinità nascoste” e quindi prodotti da suggerire più consoni di altri.

Per poter cominciare a capire il funzionamento di questi sistemi, in cui la componente collaborativa è fondante per il successo del business model di aziende come quelle sopra citate, senza impazzire dietro a formule astruse o incomprensibili vi propongo questo breve ebook della O’Reilly (cliccate sul link e dopo la compilazione del form lo potrete scaricare) dove sono accennati alcuni semplici concetti su cui si basano le implementazioni di sistemi del genere.

Practical Machine Learning: Innovations in Recommendation

Practical Machine Learning: Innovations in Recommendation

Se invece avete delle solide basi statistiche e volete approfondire il tema in maniera dettagliata, vi suggerisco di comprare e leggere uno dei manuali più completi in circolazione dal titolo “Recommender Systems: An Introduction“, cominciando magari a scaricare gratuitamente le slide riepilogative dei capitoli dello stesso libro.

Buona lettura e buon divertimento.